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T cell receptor beta (TRB) sequences were recovered from the Cancer Genome Atlas Uveal Melanoma blood exome files. Intrinsic disorder scores for amino acid (AA) sequences of the entire TRB variable region were obtained and evaluated as potentially representative of overall survival (OS) distinctions, i.e., for cases representing the upper and lower 50th percentiles for intrinsic disorder scores. Analyses using four intrinsic disorder assessment tools indicated that a lower intrinsic disorder of the blood-sourced TRB variable regions, including continuous AA sequences of the V-gene segment, the complementarity-determining region-3, and the J-gene segment, was associated with a better OS probability (with log-rank p-values ranging from 0.002 to 0.014). We further determined that intrinsic disorder assessments could be used for OS stratification for a second, immunologically cold cancer: MYCN amplified neuroblastoma. Thus, intrinsic disorder assessments of blood-sourced, full TRB variable regions may provide a novel patient stratification approach for patients with immunologically cold cancers. [ABSTRACT FROM AUTHOR]

Compositionally biased regions (CBRs), ie, tracts that are dominated by a subset of residue types, are common features of eukaryotic proteins. These are often found bounded within or almost coterminous with intrinsically disordered or 'natively unfolded' parts. Here, it is investigated how the function of such intrinsically disordered compositionally biased regions (ID-CBRs) is directly linked to their compositional traits, focusing on the well-characterized yeast (Saccharomyces cerevisiae) proteome as a test case. The ID-CBRs that are clustered together using compositional distance are discovered to have clear functional linkages at various levels of diversity. The specific case of the Sup35p and Rnq1p proteins that underlie causally linked prion phenomena ([PSI+] and [RNQ+]) is highlighted. Their prion-forming ID-CBRs are typically clustered very close together indicating some compositional engendering for [RNQ+] seeding of [PSI+] prions. Delving further, ID-CBRs with distinct types of residue patterning such as 'blocking' or relative segregation of residues into homopeptides are found to have significant functional trends. Specific examples of such ID-CBR functional linkages that are discussed are: Q/N-rich ID-CBRs linked to transcriptional coactivation, S-rich to transcription-factor binding, R-rich to DNA-binding, S/E-rich to protein localization, and D-rich linked to chromatin remodelling. These data may be useful in informing experimental hypotheses for proteins containing such regions. [ABSTRACT FROM AUTHOR]

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis. [ABSTRACT FROM AUTHOR]

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme that converts vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents supplied by its redox partner through thiol–disulphide exchange reactions. The functionally related molecular complexes assembled during this process have never been described, except for a proposed de novo model of a 'precursor' complex of hVKORC1 associated with protein disulphide isomerase (PDI). Using numerical approaches (in silico modelling and molecular dynamics simulation), we generated alternative 3D models for each molecular complex bonded either covalently or non-covalently. These models differ in the orientation of the PDI relative to hVKORC1 and in the cysteine residue involved in forming protein–protein disulphide bonds. Based on a comparative analysis of these models' shape, folding, and conformational dynamics, the most probable putative complexes, mimicking the 'precursor', 'intermediate', and 'successor' states, were suggested. In addition, we propose using these complexes to develop the 'allo-network drugs' necessary for treating blood diseases. [ABSTRACT FROM AUTHOR]

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios. [ABSTRACT FROM AUTHOR]

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme transforming vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents delivered by its redox partner through thiol-disulfide exchange reactions. The luminal loop (L-loop) is the principal mediator of hVKORC1 activation, and it is a region frequently harbouring numerous missense mutations. Four L-loop hVKORC1 mutants, suggested in vitro as either resistant (A41S, H68Y) or completely inactive (S52W, W59R), were studied in the oxidised state by numerical approaches (in silico). The DYNASOME and POCKETOME of each mutant were characterised and compared to the native protein, recently described as a modular protein composed of the structurally stable transmembrane domain (TMD) and the intrinsically disordered L-loop, exhibiting quasi-independent dynamics. The DYNASOME of mutants revealed that L-loop missense point mutations impact not only its folding and dynamics, but also those of the TMD, highlighting a strong mutation-specific interdependence between these domains. Another consequence of the mutation-induced effects manifests in the global changes (geometric, topological, and probabilistic) of the newly detected cryptic pockets and the alternation of the recognition properties of the L-loop with its redox protein. Based on our results, we postulate that (i) intra-protein allosteric regulation and (ii) the inherent allosteric regulation and cryptic pockets of each mutant depend on its DYNASOME; and (iii) the recognition of the redox protein by hVKORC1 (INTERACTOME) depend on their DYNASOME. This multifaceted description of proteins produces "omics" data sets, crucial for understanding the physiological processes of proteins and the pathologies caused by alteration of the protein properties at various "omics" levels. Additionally, such characterisation opens novel perspectives for the development of "allo-network drugs" essential for the treatment of blood disorders. [ABSTRACT FROM AUTHOR]

For about 30 years, SPEN has been the subject of research in many different fields due to its variety of functions and its conservation throughout a wide spectrum of species, like worms, arthropods, and vertebrates. To date, 216 orthologues have been documented. SPEN had been studied for its role in gene regulation in the context of cell signaling, including the NOTCH or nuclear hormone receptor signaling pathways. More recently, SPEN has been identified as a major regulator of initiation of chromosome-wide gene silencing during X chromosome inactivation (XCI) in mammals, where its function remains to be fully understood. Dependent on the biological context, SPEN functions via mechanisms which include different domains. While some domains of SPEN are highly conserved in sequence and secondary structure, species-to-species differences exist that might lead to mechanistic differences. Initiation of XCI appears to be different between humans and mice, which raises additional questions about the extent of generalization of SPEN's function in XCI. In this review, we dissect the mechanism of SPEN in XCI. We discuss its subregions and domains, focusing on its role as a major regulator. We further highlight species-related research, specifically of mouse and human SPEN, with the aim to reveal and clarify potential species-to-species differences in SPEN's function. [ABSTRACT FROM AUTHOR]

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.

The evolution and diversification of proteins capable of remodeling domains has been critical for transcriptional reprogramming during cell fate determination in multicellular eukaryotes. Chromatin remodeling proteins of the CHD3 family have been shown to have important and antagonistic impacts on seed development in the model plant, Arabidopsis thaliana , yet the basis of this functional divergence remains unknown. In this study, we demonstrate that genes encoding the CHD3 proteins PICKLE (PKL) and PICKLE-RELATED 2 (PKR2) originated from a duplication event during the diversification of crown Brassicaceae, and that these homologs have undergone distinct evolutionary trajectories since this duplication, with PKR2 fast evolving under positive selection, while PKL is subject to purifying selection. We find that the rapid evolution of PKR2 under positive selection reduces the encoded protein's intrinsic disorder, possibly suggesting a tertiary structure configuration which differs from that of PKL. Our whole genome transcriptome analysis in seeds of pkr2 and pkl mutants reveals that they act antagonistically on the expression of specific sets of genes, providing a basis for their differing roles in seed development. Our results provide insights into how gene duplication and neofunctionalization can lead to differing and antagonistic selective pressures on transcriptomes during plant reproduction, as well as on the evolutionary diversification of the CHD3 family within seed plants. [ABSTRACT FROM AUTHOR]

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein–protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid–liquid phase separation (LLPS) probability, “nuclear” localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down. [ABSTRACT FROM AUTHOR]

In recent years, it has become clear that intrinsically disordered protein segments play diverse functional roles in many cellular processes, thus leading to a reassessment of the classical structure–function paradigm. One class of intrinsically disordered protein segments is entropic clocks, corresponding to unstructured random protein chains involved in timing cellular processes. Such clocks were shown to modulate ion channel processes underlying action potential generation, propagation, and transmission. In this review, we survey the role of entropic clocks in timing intra- and inter-molecular binding events of voltage-activated potassium channels involved in gating and clustering processes, respectively, and where both are known to occur according to a similar 'ball and chain' mechanism. We begin by delineating the thermodynamic and timing signatures of a 'ball and chain'-based binding mechanism involving entropic clocks, followed by a detailed analysis of the use of such a mechanism in the prototypical Shaker voltage-activated K+ channel model protein, with particular emphasis on ion channel clustering. We demonstrate how 'chain'-level alternative splicing of the Kv channel gene modulates entropic clock-based 'ball and chain' inactivation and clustering channel functions. As such, the Kv channel model system exemplifies how linkage between alternative splicing and intrinsic disorder enables the functional diversity underlying changes in electrical signaling. [ABSTRACT FROM AUTHOR]

The complex between lipoprotein lipase (LPL) and its endothelial receptor (GPIHBP1) is responsible for the lipolytic processing of triglyceride-rich lipoproteins (TRLs) along the capillary lumen, a physiologic process that releases lipid nutrients for vital organs such as heart and skeletal muscle. LPL activity is regulated in a tissue-specific manner by endogenous inhibitors (angiopoietin-like [ANGPTL] proteins 3, 4, and 8), but the molecular mechanisms are incompletely understood. ANGPTL4 catalyzes the inactivation of LPL monomers by triggering the irreversible unfolding of LPL's α/β-hydrolase domain. Here, we show that this unfolding is initiated by the binding of ANGPTL4 to sequences near LPL's catalytic site, including β2, β3-α3, and the lid. Using pulse-labeling hydrogen‒deuterium exchange mass spectrometry, we found that ANGPTL4 binding initiates conformational changes that are nucleated on β3-α3 and progress to β5 and β4-α4, ultimately leading to the irreversible unfolding of regions that form LPL's catalytic pocket. LPL unfolding is context dependent and varies with the thermal stability of LPL's α/β-hydrolase domain (Tm of 34.8 °C). GPIHBP1 binding dramatically increases LPL stability (Tm of 57.6 °C), while ANGPTL4 lowers the onset of LPL unfolding by ∼20 °C, both for LPL and LPL•GPIHBP1 complexes. These observations explain why the binding of GPIHBP1 to LPL retards the kinetics of ANGPTL4-mediated LPL inactivation at 37 °C but does not fully suppress inactivation. The allosteric mechanism by which ANGPTL4 catalyzes the irreversible unfolding and inactivation of LPL is an unprecedented pathway for regulating intravascular lipid metabolism.

Cooperativity enhances the responsiveness of biomolecular receptors to small changes in the concentration of their target ligand, albeit with a concomitant reduction in affinity. The binding midpoint of a two-site receptor with a Hill coefficient of 1.9, for example, must be at least 19 times higher than the dissociation constant of the higher affinity of its two binding sites. This trade-off can be overcome, however, by the extra binding energy provided by the addition of more binding sites, which can be used to achieve highly cooperative receptors that still retain high affinity. Exploring this experimentally, we have employed an "intrinsic disorder" mechanism to design two cooperative, three-binding-site receptors starting from a single-site-and thus noncooperative-doxorubicin-binding aptamer. The first receptor follows a binding energy landscape that partitions the energy provided by the additional binding event to favor affinity, achieving a Hill coefficient of 1.9 but affinity within a factor of 2 of the parent aptamer. The binding energy landscape of the second receptor, in contrast, partitions more of this energy toward cooperativity, achieving a Hill coefficient of 2.3, but at the cost of 4-fold poorer affinity than that of the parent aptamer. The switch between these two behaviors is driven primarily by the affinity of the receptors' second binding event, which serves as an allosteric "gatekeeper" defining the extent to which the system is weighted toward higher cooperativity or higher affinity.

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.

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.

The WRKY transcription factors are involved in a range of biological processes in plants, including the response to biotic and abiotic stresses and plant immunity. There is also evidence that intrinsic disorder proteins (IDPs) are involved in key cellular functions such as regulating biological processes. Here, the intrinsic disorder distribution of WRKYs within T. turgidium and its protein-protein interactions are investigated. The analysis showed that the hub proteins have higher level of disorder content in T. turgidium. In comparison to other cereals, it is shown the intrinsic features in T. turgidium are evolutionary conserved, which may explain the multifunctionality of WRKYs in plants.

LC8, a ubiquitous and highly conserved hub protein, binds over 100 proteins involved in numerous cellular functions, including cell death, signaling, tumor suppression, and viral infection. LC8 binds intrinsically disordered proteins (IDPs), and although several of these contain multiple LC8 binding motifs, the effects of multivalency on complex formation are unclear. Drosophila ASCIZ has seven motifs that vary in sequence and inter-motif linker lengths, especially within subdomain QT2–4 containing the second, third, and fourth LC8 motifs. Using isothermal-titration calorimetry, analytical-ultracentrifugation, and native mass-spectrometry of QT2–4 variants, with methodically deactivated motifs, we show that inter-motif spacing and specific motif sequences combine to control binding affinity and compositional heterogeneity of multivalent duplexes. A short linker separating strong and weak motifs results in stable duplexes but forms off-register structures at high LC8 concentrations. Contrastingly, long linkers engender lower cooperativity and heterogeneous complexation at low LC8 concentrations. Accordingly, two-mers, rather than the expected three-mers, dominate negative-stain electron-microscopy images of QT2–4. Comparing variants containing weak-strong and strong-strong motif combinations demonstrates sequence also regulates IDP/LC8 assembly. The observed trends persist for trivalent ASCIZ subdomains: QT2–4, with long and short linkers, forms heterogeneous complexes, whereas QT4–6, with similar mid-length linkers, forms homogeneous complexes. Implications of linker length variations for function are discussed. [ABSTRACT FROM AUTHOR]

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD’s predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme transforming vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents delivered by its redox partner through thiol-disulfide exchange reactions. The luminal loop (L-loop) is the principal mediator of hVKORC1 activation, and it is a region frequently harbouring numerous missense mutations. Four L-loop hVKORC1 mutants, suggested in vitro as either resistant (A41S, H68Y) or completely inactive (S52W, W59R), were studied in the oxidised state by numerical approaches (in silico). The DYNASOME and POCKETOME of each mutant were characterised and compared to the native protein, recently described as a modular protein composed of the structurally stable transmembrane domain (TMD) and the intrinsically disordered L-loop, exhibiting quasi-independent dynamics. The DYNASOME of mutants revealed that L-loop missense point mutations impact not only its folding and dynamics, but also those of the TMD, highlighting a strong mutation-specific interdependence between these domains. Another consequence of the mutation-induced effects manifests in the global changes (geometric, topological, and probabilistic) of the newly detected cryptic pockets and the alternation of the recognition properties of the L-loop with its redox protein. Based on our results, we postulate that (i) intra-protein allosteric regulation and (ii) the inherent allosteric regulation and cryptic pockets of each mutant depend on its DYNASOME; and (iii) the recognition of the redox protein by hVKORC1 (INTERACTOME) depend on their DYNASOME. This multifaceted description of proteins produces “omics” data sets, crucial for understanding the physiological processes of proteins and the pathologies caused by alteration of the protein properties at various “omics” levels. Additionally, such characterisation opens novel perspectives for the development of “allo-network drugs” essential for the treatment of blood disorders.

This article is a Commentary on Gómez‐Pérez et al. (2023), 239: 2320–2334. [ABSTRACT FROM AUTHOR]

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein–protein and protein–nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/. [ABSTRACT FROM AUTHOR]

Intrinsically disordered regions in proteins have been shown to be important in protein function. However, not all proteins contain the same amount of intrinsic disorder. The variation in the levels of intrinsic disorder in different types of proteins has been extensively studied over the last two decades. It is now known that the levels of intrinsic disorder vary in proteins across organisms, functions, diseases, and cellular locations. This review consolidates the known trends in the abundance of intrinsic disorder identified in groups of proteins across varying conditions and functions. It also presents new data towards the understanding of intrinsic disorder in cell type-specific proteins. [ABSTRACT FROM AUTHOR]

For about 30 years, SPEN has been the subject of research in many different fields due to its variety of functions and its conservation throughout a wide spectrum of species, like worms, arthropods, and vertebrates. To date, 216 orthologues have been documented. SPEN had been studied for its role in gene regulation in the context of cell signaling, including the NOTCH or nuclear hormone receptor signaling pathways. More recently, SPEN has been identified as a major regulator of initiation of chromosome-wide gene silencing during X chromosome inactivation (XCI) in mammals, where its function remains to be fully understood. Dependent on the biological context, SPEN functions via mechanisms which include different domains. While some domains of SPEN are highly conserved in sequence and secondary structure, species-to-species differences exist that might lead to mechanistic differences. Initiation of XCI appears to be different between humans and mice, which raises additional questions about the extent of generalization of SPEN’s function in XCI. In this review, we dissect the mechanism of SPEN in XCI. We discuss its subregions and domains, focusing on its role as a major regulator. We further highlight species-related research, specifically of mouse and human SPEN, with the aim to reveal and clarify potential species-to-species differences in SPEN’s function.

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.

Inflammation induced by nonspecific pathogenic or endogenous danger signals is an essential mechanism of innate immune response. The innate immune responses are rapidly triggered by conserved germline-encoded receptors that recognize broad patterns indicative of danger, with subsequent signal amplification by modular effectors, which have been the subject of intense investigation for many years. Until recently, however, the critical role of intrinsic disorder-driven phase separation in facilitating innate immune responses went largely unappreciated. In this review, we discuss emerging evidences that many innate immune receptors, effectors, and/or interactors function as “all-or-nothing” switch-like hubs to stimulate acute and chronic inflammation. By concentrating or relegating modular signaling components to phase-separated compartments, cells construct flexible and spatiotemporal distributions of key signaling events to ensure rapid and effective immune responses to a myriad of potentially harmful stimuli.

Prions are proteinaceous particles that can propagate an alternative conformation to further copies of the same protein. They have been described in mammals, fungi, bacteria and archaea. Furthermore, across diverse organisms from bacteria to eukaryotes, prion-like proteins that have similar sequence characters are evident. Such prion-like proteins have been linked to pathomechanisms of amyotrophic lateral sclerosis (ALS) in humans, in particular TDP43, FUS, TAF15, EWSR1 and hnRNPA2. Because of the desire to study human disease-linked proteins in model organisms, and to gain insights into the functionally important parts of these proteins and how they have changed across hundreds of millions of years of evolution, we analyzed how the sequence traits of these five proteins have evolved across eukaryotes, including plants and metazoa. We discover that the RNA-binding domain architecture of these proteins is deeply conserved since their emergence. Prion-like regions are also deeply and widely conserved since the origination of the protein families for FUS, TAF15 and EWSR1, and since the last common ancestor of metazoa for TDP43 and hnRNPA2. Prion-like composition is uncommon or weak in any plant orthologs observed, however in TDP43 many plant proteins have equivalent regions rich in other amino acids (namely glycine and tyrosine and/or serine) that may be linked to stress granule recruitment. Deeply conserved low-complexity domains are identified that likely have functional significance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Labelling techniques such as electron paramagnetic resonance spectroscopy and single-molecule fluorescence resonance energy transfer, allow access to distances in the range of tens of angstroms, corresponding to the size of proteins and small to medium-sized protein complexes. Such measurements do not require long-range ordering and are therefore applicable to systems with partial disorder. Data from spin-label-based measurements can be processed into distance distributions that provide information about the extent of such disorder. Using such information in modelling presents several challenges, including a small number of restraints, the influence of the label itself on the measured distance and distribution width, and balancing the fitting quality of the long-range restraints with the fitting quality of other restraint subsets. Starting with general considerations about integrative and hybrid structural modelling, this review provides an overview of recent approaches to these problems and identifies where further progress is needed. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinases (CDKs) are presumed to control the cell cycle by phosphorylating a large number of proteins involved in S-phase and mitosis, two mechanistically disparate biological processes. While the traditional qualitative model of CDK-mediated cell cycle control relies on differences in inherent substrate specificity between distinct CDK-cyclin complexes, they are largely dispensable according to the opposing quantitative model, which states that changes in the overall CDK activity level promote orderly progression through S-phase and mitosis. However, a mechanistic explanation for how such an activity can simultaneously regulate many distinct proteins is lacking. New evidence suggests that the CDK-dependent phosphorylation of ostensibly very diverse proteins might be achieved due to underlying similarity of phosphorylation sites and of the biochemical effects of their phosphorylation: they are preferentially located within intrinsically disordered regions of proteins that are components of membraneless organelles, and they regulate phase separation. Here, we review this evidence and suggest a mechanism for how a single enzyme's activity can generate the dynamics required to remodel the cell at mitosis. [ABSTRACT FROM AUTHOR]

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the "generic" disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes. [ABSTRACT FROM AUTHOR]

Much of plant development depends on cell-to-cell redistribution of the plant hormone auxin, which is facilitated by the plasma membrane (PM) localized PIN FORMED (PIN) proteins. Auxin export activity, developmental roles, subcellular trafficking, and polarity of PINs have been well studied, but their structure remains elusive besides a rough outline that they contain two groups of 5 alpha-helices connected by a large hydrophilic loop (HL). Here, we focus on the PIN1 HL as we could produce it in sufficient quantities for biochemical investigations to provide insights into its secondary structure. Circular dichroism (CD) studies revealed its nature as an intrinsically disordered protein (IDP), manifested by the increase of structure content upon thermal melting. Consistent with IDPs serving as interaction platforms, PIN1 loops homodimerize. PIN1 HL cytoplasmic overexpression in Arabidopsis disrupts early endocytic trafficking of PIN1 and PIN2 and causes defects in the cotyledon vasculature formation. In summary, we demonstrate that PIN1 HL has an intrinsically disordered nature, which must be considered to gain further structural insights. Some secondary structures may form transiently during pairing with known and yet-to-be-discovered interactors. [ABSTRACT FROM AUTHOR]

Before the SARS-CoV-2 Omicron variant emergence, shell disorder models (SDM) suggested that an attenuated precursor from pangolins may have entered humans in 2017 or earlier. This was based on a shell disorder analysis of SARS-CoV-1/2 and pangolin-Cov-2017. The SDM suggests that Omicron is attenuated with almost identical N (inner shell) disorder as pangolin-CoV-2017 (N-PID (percentage of intrinsic disorder): 44.8% vs. 44.9%—lower than other variants). The outer shell disorder (M-PID) of Omicron is lower than that of other variants and pangolin-CoV-2017 (5.4% vs. 5.9%). COVID-19-related CoVs have the lowest M-PIDs (hardest outer shell) among all CoVs. This is likely to be responsible for the higher contagiousness of SARS-CoV-2 and Omicron, since hard outer shell protects the virion from salivary/mucosal antimicrobial enzymes. Phylogenetic study using M reveals that Omicron branched off from an ancestor of the Wuhan-Hu-1 strain closely related to pangolin-CoVs. M, being evolutionarily conserved in COVID-19, is most ideal for COVID-19 phylogenetic study. Omicron may have been hiding among burrowing animals (e.g., pangolins) that provide optimal evolutionary environments for attenuation and increase shell hardness, which is essential for fecal–oral–respiratory transmission via buried feces. Incoming data support SDM e.g., the presence of fewer infectious particles in the lungs than in the bronchi upon infection. [ABSTRACT FROM AUTHOR]

The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β2-microgloblulin (β2m) can be captured and investigated. [ABSTRACT FROM AUTHOR]

A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus’s predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/.

The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β2-microgloblulin (β2m) can be captured and investigated.

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/ [ABSTRACT FROM AUTHOR]

The nucleocapsid (N) protein is conserved in all four genera of the coronaviruses, namely alpha, beta, gamma, and delta, and is essential for genome functionality. Bioinformatic analysis of coronaviral N sequences revealed two intrinsically disordered regions (IDRs) at the center of the polypeptide. While both IDR structures were found in alpha, beta, and gamma-coronaviruses, the second IDR was absent in deltacoronaviruses. Two novel coronaviruses, currently placed in the Gammacoronavirus genus, appeared intermediate in this regard, as the second IDR structure could be barely discerned with a low probability of disorder. Interestingly, these two are the only coronaviruses thus far isolated from marine mammals, namely beluga whale and bottlenose dolphin, two highly related species; the N proteins of the viruses were also virtually identical, differing by a single amino acid. These two unique viruses remain phylogenetic oddities, since gammacoronaviruses are generally avian (bird) in nature. Lastly, both IDRs, regardless of the coronavirus genus in which they occurred, were rich in Ser and Arg, in agreement with their disordered structure. It is postulated that the central IDRs make cardinal contributions in the multitasking role of the nucleocapsid protein, likely requiring structural plasticity, perhaps also impinging on coronavirus host tropism and cross-species transmission.

LC8, a ubiquitous and highly conserved hub protein, binds over 100 proteins involved in numerous cellular functions, including cell death, signaling, tumor suppression, and viral infection. LC8 binds intrinsically disordered proteins (IDPs), and although several of these contain multiple LC8 binding motifs, the effects of multivalency on complex formation are unclear. Drosophila ASCIZ has seven motifs that vary in sequence and inter-motif linker lengths, especially within subdomain QT2–4 containing the second, third, and fourth LC8 motifs. Using isothermal-titration calorimetry, analytical-ultracentrifugation, and native mass-spectrometry of QT2–4 variants, with methodically deactivated motifs, we show that inter-motif spacing and specific motif sequences combine to control binding affinity and compositional heterogeneity of multivalent duplexes. A short linker separating strong and weak motifs results in stable duplexes but forms off-register structures at high LC8 concentrations. Contrastingly, long linkers engender lower cooperativity and heterogeneous complexation at low LC8 concentrations. Accordingly, two-mers, rather than the expected three-mers, dominate negative-stain electron-microscopy images of QT2–4. Comparing variants containing weak-strong and strong-strong motif combinations demonstrates sequence also regulates IDP/LC8 assembly. The observed trends persist for trivalent ASCIZ subdomains: QT2–4, with long and short linkers, forms heterogeneous complexes, whereas QT4–6, with similar mid-length linkers, forms homogeneous complexes. Implications of linker length variations for function are discussed.

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.

Compositionally-biased (CB) regions in biological sequences are enriched for a subset of sequence residue types. These can be shorter regions with a concentrated bias (i.e., those termed 'low-complexity'), or longer regions that have a compositional skew. These regions comprise a prominent class of the uncharacterized 'dark matter' of the protein universe. Here, I report the latest version of the fLPS package for the annotation of CB regions, which includes added consideration of DNA sequences, to label the eight possible biased regions of DNA. In this version, the user is now able to restrict analysis to a specified subset of residue types, and also to filter for previously annotated domains to enable detection of discontinuous CB regions. A 'thorough' option has been added which enables the labelling of subtler biases, typically made from a skew for several residue types. In the output, protein CB regions are now labelled with bias classes reflecting the physico-chemical character of the biasing residues. The fLPS 2.0 package is available from: https://github.com/pmharrison/flps2 or in a Supplemental File of this paper. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) interact with globular proteins through a variety of mechanisms, resulting in the structurally heterogeneous ensembles known as fuzzy complexes. While there exists a reasonable comprehension on how IDP sequence determines the unbound IDP ensemble, little is known about what shapes the structural characteristics of IDPs bound to their targets. Using a statistical thermodynamic model, we show that the targetbound ensembles are determined by a simple code that combines the IDP sequence and the distribution of IDP-target interaction hotspots. These two parameters define the conformational space of target-bound IDPs and rationalize the observed structural heterogeneity of fuzzy complexes. The presented model successfully reproduces the dynamical signatures of target-bound IDPs from the NMR relaxation experiments as well as the changes of interaction affinity and the IDP helicity induced by mutations. The model explains how the target-bound IDP ensemble adapts to mutations in order to achieve an optimal balance between conformational freedom and interaction energy. Taken together, the presented sequence-ensemble relationship of fuzzy complexes explains the different manifestations of IDP disorder in folding-upon-binding processes. [ABSTRACT FROM AUTHOR]

SummaryThe biogenesis of autophagosomes commences at the phagophore assembly site (PAS), a protein-vesicle ultrastructure that is organized by the Atg1 complex. The Atg1 complex consists of the Atg1 protein kinase, the intrinsically disordered region-rich Atg13, and the dimeric double crescent-shaped Atg17-Atg31-Atg29 subcomplex. We show that the PAS contains a relatively uniform ~28 copies of Atg17, and upon autophagy induction, similar numbers of Atg1 and Atg13 molecules. We then apply ensemble refinement of small angle x-ray scattering (SAXS) to determine the solution structures of the Atg1-Atg13 and Atg17-Atg31-Atg29 subcomplexes and the Atg1 complex, using a trimmed “mini-pentamer” tractable to biophysical studies. We observe tetramers of Atg1 pentamers that assemble via Atg17-Atg31-Atg29. This leads to a model for the higher organization of the Atg1 complex in PAS scaffolding.

Our notions of protein function have long been determined by the protein structure–function paradigm. However, the idea that protein function is dictated by a prerequisite complementarity of shapes at the binding interface is becoming increasingly challenged. Interactions involving intrinsically disordered proteins (IDPs) have indicated a significant degree of disorder present in the bound state, ranging from static disorder to complete disorder, termed ‘random fuzziness’. This review assesses the anatomy of an IDP and relates how its intrinsic properties permit promiscuity and allow for the various modes of interaction. Furthermore, a mechanistic overview of the types of disordered domains is detailed, while also relating to a recent example and the kinetic and thermodynamic principles governing its formation.

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.

Labelling techniques such as electron paramagnetic resonance spectroscopy and single-molecule fluorescence resonance energy transfer, allow access to distances in the range of tens of angstroms, corresponding to the size of proteins and small to medium-sized protein complexes. Such measurements do not require long-range ordering and are therefore applicable to systems with partial disorder. Data from spin-label-based measurements can be processed into distance distributions that provide information about the extent of such disorder. Using such information in modelling presents several challenges, including a small number of restraints, the influence of the label itself on the measured distance and distribution width, and balancing the fitting quality of the long-range restraints with the fitting quality of other restraint subsets. Starting with general considerations about integrative and hybrid structural modelling, this review provides an overview of recent approaches to these problems and identifies where further progress is needed.

Calmodulin (CaM) is a calcium-binding protein that transduces signals to downstream proteins through target binding upon calcium binding in a time-dependent manner. Understanding the target binding process that tunes CaM’s affinity for the calcium ions (Ca2+), or vice versa, may provide insight into how Ca2+-CaM selects its target binding proteins. However, modeling of Ca2+-CaM in molecular simulations is challenging because of the gross structural changes in its central linker regions while the two lobes are relatively rigid due to tight binding of the Ca2+ to the calcium-binding loops where the loop forms a pentagonal bipyramidal coordination geometry with Ca2+. This feature that underlies the reciprocal relation between Ca2+ binding and target binding of CaM, however, has yet to be considered in the structural modeling. Here, we presented a coarse-grained model based on the Associative memory, Water mediated, Structure, and Energy Model (AWSEM) protein force field, to investigate the salient features of CaM. Particularly, we optimized the force field of CaM and that of Ca2+ ions by using its coordination chemistry in the calcium-binding loops to match with experimental observations. We presented a “community model” of CaM that is capable of sampling various conformations of CaM, incorporating various calcium-binding states, and carrying the memory of binding with various targets, which sets the foundation of the reciprocal relation of target binding and Ca2+ binding in future studies.

Intravascular processing of triglyceride-rich lipoproteins (TRLs) is crucial for delivery of dietary lipids fueling energy metabolism in heart and skeletal muscle and for storage in white adipose tissue. During the last decade, mechanisms underlying focal lipolytic processing of TRLs along the luminal surface of capillaries have been clarified by fresh insights into the functions of lipoprotein lipase (LPL); LPL’s dedicated transporter protein, glycosylphosphatidylinositol-anchored high density lipoprotein–binding protein 1 (GPIHBP1); and its endogenous inhibitors, angiopoietin-like (ANGPTL) proteins 3, 4, and 8. Key discoveries in LPL biology include solving the crystal structure of LPL, showing LPL is catalytically active as a monomer rather than as a homodimer, and that the borderline stability of LPL’s hydrolase domain is crucial for the regulation of LPL activity. Another key discovery was understanding how ANGPTL4 regulates LPL activity. The binding of ANGPTL4 to LPL sequences adjacent to the catalytic cavity triggers cooperative and sequential unfolding of LPL’s hydrolase domain resulting in irreversible collapse of the catalytic cavity and loss of LPL activity. Recent studies have highlighted the importance of the ANGPTL3–ANGPTL8 complex for endocrine regulation of LPL activity in oxidative organs (e.g., heart, skeletal muscle, brown adipose tissue), but the molecular mechanisms have not been fully defined. New insights have also been gained into LPL–GPIHBP1 interactions and how GPIHBP1 moves LPL to its site of action in the capillary lumen. GPIHBP1 is an atypical member of the LU (Ly6/uPAR) domain protein superfamily, containing an intrinsically disordered and highly acidic N-terminal extension and a disulfide bond–rich three-fingered LU domain. Both the disordered acidic domain and the folded LU domain are crucial for the stability and transport of LPL, and for modulating its susceptibility to ANGPTL4-mediated unfolding. This review focuses on recent advances in the biology and biochemistry of crucial proteins for intravascular lipolysis.

Ubiquitin is a small protein at the heart of many cellular processes, and several different protein domains are known to recognize and bind ubiquitin. A common motif for interaction with ubiquitin is the Ubiquitin Interacting Motif (UIM), characterized by a conserved sequence signature and often found in multi-domain proteins. Multi-domain proteins with intrinsically disordered regions mediate interactions with multiple partners, orchestrating diverse pathways. Short linear motifs for binding are often embedded in these disordered regions and play crucial roles in modulating protein function. In this work, we investigated the structural propensities of UIMs using molecular dynamics simulations and NMR chemical shifts. Despite the structural portrait depicted by X-crystallography of stable helical structures, we show that UIMs feature both helical and intrinsically disordered conformations. Our results shed light on a new class of disordered UIMs. This group is here exemplified by the C-terminal domain of one isoform of ataxin-3 and a group of ubiquitin-specific proteases. Intriguingly, UIMs not only bind ubiquitin. They can be a recruitment point for other interactors, such as parkin and the heat shock protein Hsc70-4. Disordered UIMs can provide versatility and new functions to the client proteins, opening new directions for research on their interactome.