Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning

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/.

Protein conformational dynamics and phenotypic switching

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure but exist as conformational ensembles. Because of their structural plasticity, they can interact with multiple partners. The protein interactions between IDPs and their partners form scale-free protein interaction networks (PINs) that facilitate information flow in the cell. Because of their plasticity, IDPs typically occupy hub positions in cellular PINs. Furthermore, their conformational dynamics and propensity for post-translational modifications contribute to "conformational" noise which is distinct from the well-recognized transcriptional noise. Therefore, upregulation of IDPs in response to a specific input, such as stress, contributes to increased noise and, hence, an increase in stochastic, "promiscuous" interactions. These interactions lead to activation of latent pathways or can induce "rewiring" of the PIN to yield an optimal output underscoring the critical role of IDPs in regulating information flow. We have used PAGE4, a highly intrinsically disordered stress-response protein as a paradigm. Employing a variety of experimental and computational techniques, we have elucidated the role of PAGE4 in phenotypic switching of prostate cancer cells at a systems level. These cumulative studies over the past decade provide a conceptual framework to better understand how IDP conformational dynamics and conformational noise might facilitate cellular decision-making.

Dynamic, but Not Necessarily Disordered, Human-Virus Interactions Mediated through SLiMs in Viral Proteins

Most viruses have small genomes that encode proteins needed to perform essential enzymatic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Bioinformatic Analysis of Lytic Polysaccharide Monooxygenases Reveals the Pan-Families Occurrence of Intrinsically Disordered C-Terminal Extensions

Lytic polysaccharide monooxygenases (LPMOs) are monocopper enzymes secreted by many organisms and viruses. LPMOs catalyze the oxidative cleavage of different types of polysaccharides and are today divided into eight families (AA9-11, AA13-17) within the Auxiliary Activity enzyme class of the CAZy database. LPMOs minimal architecture encompasses a catalytic domain, to which can be appended a carbohydrate-binding module. Intriguingly, we observed that some LPMO sequences also display a C-terminal extension of varying length not associated with any known function or fold. Here, we analyzed 27,060 sequences from different LPMO families and show that 60% have a C-terminal extension predicted to be intrinsically disordered. Our analysis shows that these disordered C-terminal regions (dCTRs) are widespread in all LPMO families (except AA13) and differ in terms of sequence length and amino-acid composition. Noteworthily, these dCTRs have so far only been observed in LPMOs. LPMO-dCTRs share a common polyampholytic nature and an enrichment in serine and threonine residues, suggesting that they undergo post-translational modifications. Interestingly, dCTRs from AA11 and AA15 are enriched in redox-sensitive, conditionally disordered regions. The widespread occurrence of dCTRs in LPMOs from evolutionarily very divergent organisms, hints at a possible functional role and opens new prospects in the field of LPMOs.

Transcription Factor Dynamics

To predict transcription, one needs a mechanistic understanding of how the numerous required transcription factors (TFs) explore the nuclear space to find their target genes, assemble, cooperate, and compete with one another. Advances in fluorescence microscopy have made it possible to visualize real-time TF dynamics in living cells, leading to two intriguing observations: first, most TFs contact chromatin only transiently; and second, TFs can assemble into clusters through their intrinsically disordered regions. These findings suggest that highly dynamic events and spatially structured nuclear microenvironments might play key roles in transcription regulation that are not yet fully understood. The emerging model is that while some promoters directly convert TF-binding events into on/off cycles of transcription, many others apply complex regulatory layers that ultimately lead to diverse phenotypic outputs. Cracking this kinetic code is an ongoing and challenging task that is made possible by combining innovative imaging approaches with biophysical models.

Intrinsic disorder in protein kinase A anchoring proteins signaling complexes

Protein kinase A (PKA) is regulated by a diverse class of anchoring proteins known as AKAPs that target PKA to subsets of its activators and substrates. Recently, it was reported that PKA can remain bound to its regulatory subunit after activation in contrast to classical model of activation-by-dissociation. This implies that PKA remains bound to the AKAPs and its substrates, and thus suggest many phosphorylation reactions occur while PKA is physically connected to its substrate. Intra-complex reactions are sensitive to the architecture of the signaling complex, but generally concentration independent. We show that most AKAPs have long intrinsically disordered regions, and suggest that they represent an adaptation for intra-complex phosphorylation. Based on polymer models of the disordered proteins, we predict that the effective concentrations of tethered substrates range from the low millimolar range to tens of micromolar. Based on recent models for intra-complex enzyme reactions, we suggest that the structure of the AKAP signaling complex is likely to be source of allosteric regulation of PKA signaling.

Intrinsic Disorder in Human RNA-Binding Proteins

Although RNA-binding proteins (RBPs) are known to be enriched in intrinsic disorder, no previous analysis focused on RBPs interacting with specific RNA types. We fill this gap with a comprehensive analysis of the putative disorder in RBPs binding to six common RNA types: messenger RNA (mRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), and internal ribosome RNA (irRNA). We also analyze the amount of putative intrinsic disorder in the RNA-binding domains (RBDs) and non-RNA-binding-domain regions (non-RBD regions). Consistent with previous studies, we show that in comparison with human proteome, RBPs are significantly enriched in disorder. However, closer examination finds significant enrichment in predicted disorder for the mRNA-, rRNA- and snRNA-binding proteins, while the proteins that interact with ncRNA and irRNA are not enriched in disorder, and the tRNA-binding proteins are significantly depleted in disorder. We show a consistent pattern of significant disorder enrichment in the non-RBD regions coupled with low levels of disorder in RBDs, which suggests that disorder is relatively rarely utilized in the RNA-binding regions. Our analysis of the non-RBD regions suggests that disorder harbors posttranslational modification sites and is involved in the putative interactions with DNA. Importantly, we utilize experimental data from DisProt and independent data from Pfam to validate the above observations that rely on the disorder predictions. This study provides new insights into the distribution of disorder across proteins that bind different RNA types and the functional role of disorder in the regions where it is enriched.

Disentangling the complexity of low complexity proteins

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs.

Short abstract

There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

Next generation protein structure predictions and genetic variant interpretation

The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how better structure predictions might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants.

flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.

Molecular assessment of proteins encoded by the mitochondrial genome of Clarias batrachus and Clarias gariepinus

The population of catfish, Clarias batrachus has substantially diminished in various countries and studies show that another related species Clarias gariepinus is replacing it. The better adaptability and survivability of C. gariepinus over C. batrachus could be attributed to the metabolic differences between these two species, which is primarily regulated by mitochondrial activities. To understand the reasons behind this phenomenon, we performed in silico analyses to decipher the differences between the proteins encoded by the mitochondrial genome of these two related species. Our analysis revealed that out of thirteen, twelve proteins encoded by the mitochondrial genome of these two species have substantial variations between them. We characterised these variations by analysing their effect on secondary structure, intrinsic disorder predisposition, and functional impact on protein and stability parameters. Our data show that most of the parameters are changing between these two closely related species. Altogether, we demonstrate the molecular insights into the mitochondrial genome-encoded proteins of these two species and predict their effect on protein function and stability that might be helping C. gariepinus to gain survivability better than the C. batrachus.

Disordered RNA-Binding Region Prediction with DisoRDPbind

RNA chaperone activity is one of the many functions of intrinsically disordered regions (IDRs). IDRs function without the prerequisite of a stable structure. Instead, their functions arise from structural ensembles. A common theme in IDR function is molecular recognition; IDRs mediate interactions with other proteins, RNA, and DNA. Many computational methods are available to predict IDRs from protein sequence, but relatively few are available for predicting IDR functions. Available methods primarily focus on protein-protein interactions. DisoRDPbind was developed to predict several protein functions including interactions with RNA. This method is available as a user-friendly web interface, located at http://biomine.cs.vcu.edu/servers/DisoRDPbind/ . The development and architecture of DisoRDPbind is briefly presented, and its accuracy relative to other RNA-binding residue predictors is discussed. We explain usage of the web interface in detail and provide an example of prediction results and interpretation. While DisoRDPbind does not identify RNA chaperones directly, we provide a case study of an RNA chaperone, HCV core protein, as an example of the method's utility in the study of RNA chaperones.

Networks of Networks: An Essay on Multi-Level Biological Organization

The multi-level organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism, while in the same time each protein (a single node of such interaction network) is itself a network of interacting amino-acid residues allowing coordinated motion of the macromolecule and systemic effect as allosteric behavior. Similar pictures can be drawn for structure and function of cells, organs, tissues, and ecological systems. The majority of biologists are used to think that causally relevant events originate from the lower level (the molecular one) in the form of perturbations, that “climb up” the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such causative model, stemming from the usual genotype-phenotype distinction, is not the only one. As a matter of fact, one can observe top-down, bottom-up, as well as middle-out perturbation/control trajectories. The recent complex network studies allow to go further the pure qualitative observation of the existence of both non-linear and non-bottom-up processes and to uncover the deep nature of multi-level organization. Here, taking as paradigm protein structural and interaction networks, we review some of the most relevant results dealing with between networks communication shedding light on the basic principles of complex system control and dynamics and offering a more realistic frame of causation in biology.

Protein Aggregation Landscape in Neurodegenerative Diseases: Clinical Relevance and Future Applications

Intrinsic disorder is a natural feature of polypeptide chains, resulting in the lack of a defined three-dimensional structure. Conformational changes in intrinsically disordered regions of a protein lead to unstable β-sheet enriched intermediates, which are stabilized by intermolecular interactions with other β-sheet enriched molecules, producing stable proteinaceous aggregates. Upon misfolding, several pathways may be undertaken depending on the composition of the amino acidic string and the surrounding environment, leading to different structures. Accumulating evidence is suggesting that the conformational state of a protein may initiate signalling pathways involved both in pathology and physiology. In this review, we will summarize the heterogeneity of structures that are produced from intrinsically disordered protein domains and highlight the routes that lead to the formation of physiological liquid droplets as well as pathogenic aggregates. The most common proteins found in aggregates in neurodegenerative diseases and their structural variability will be addressed. We will further evaluate the clinical relevance and future applications of the study of the structural heterogeneity of protein aggregates, which may aid the understanding of the phenotypic diversity observed in neurodegenerative disorders.

Comprehensive Intrinsic Disorder Analysis of 6108 Viral Proteomes: From the Extent of Intrinsic Disorder Penetrance to Functional Annotation of Disordered Viral Proteins

Much of our understanding of proteins and proteomes comes from the traditional protein structure-function paradigm. However, in the last 2 decades, both computational and experimental studies have provided evidence that a large fraction of functional proteomes across different domains of life consists of intrinsically disordered proteins, thus triggering a quest to unravel and decipher protein intrinsic disorder. Unlike structured/ordered proteins, intrinsically disordered proteins/regions (IDPs/IDRs) do not possess a well-defined structure under physiological conditions and exist as highly dynamic conformational ensembles. In spite of this peculiarity, these proteins have crucial roles in cell signaling and regulation. To date, studies on the abundance and function of IDPs/IDRs in viruses are rather limited. To fill this gap, we carried out an extensive and thorough bioinformatics analysis of 283 000 proteins from 6108 reference viral proteomes. We analyzed protein intrinsic disorder from multiple perspectives, such as abundance of IDPs/IDRs across diverse virus types, their functional annotations, and subcellular localization in taxonomically divergent hosts. We show that the content of IDPs/IDRs in viral proteomes varies broadly as a function of virus genome types and taxonomically divergent hosts. We have combined the two most commonly used and accurate IDP predictors' results with charge-hydropathy (CH) versus cumulative distribution function (CDF) plots to categorize the viral proteins according to their IDR content and physicochemical properties. Mapping of gene ontology on the disorder content of viral proteins reveals that IDPs are primarily involved in key virus-host interactions and host antiviral immune response downregulation, which are reinforced by the post-translational modifications tied to disorder-enriched viral proteins. The present study offers detailed insights into the prevalence of the intrinsic disorder in viral proteomes and provides appealing targets for the design of novel therapeutics.

QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores

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/.

Insights into the evolutionary forces that shape the codon usage in the viral genome segments encoding intrinsically disordered protein regions

Intrinsically disordered regions/proteins (IDRs) are abundant across all the domains of life, where they perform important regulatory roles and supplement the biological functions of structured proteins/regions (SRs). Despite the multifunctionality features of IDRs, several interrogations on the evolution of viral genomic regions encoding IDRs in diverse viral proteins remain unreciprocated. To fill this gap, we benchmarked the findings of two most widely used and reliable intrinsic disorder prediction algorithms (IUPred2A and ESpritz) to a dataset of 6108 reference viral proteomes to unravel the multifaceted evolutionary forces that shape the codon usage in the viral genomic regions encoding for IDRs and SRs. We found persuasive evidence that the natural selection predominantly governs the evolution of codon usage in regions encoding IDRs by most of the viruses. In addition, we confirm not only that codon usage in regions encoding IDRs is less optimized for the protein synthesis machinery (transfer RNAs pool) of their host than for those encoding SRs, but also that the selective constraints imposed by codon bias sustain this reduced optimization in IDRs. Our analysis also establishes that IDRs in viruses are likely to tolerate more translational errors than SRs. All these findings hold true, irrespective of the disorder prediction algorithms used to classify IDRs. In conclusion, our study offers a novel perspective on the evolution of viral IDRs and the evolutionary adaptability to multiple taxonomically divergent hosts.

Exploring Energy Landscapes of Intrinsically Disordered Proteins: Insights into Functional Mechanisms

Intrinsically disordered proteins (IDPs) lack a rigid three-dimensional structure and populate a polymorphic ensemble of conformations. Because of the lack of a reference conformation, their energy landscape representation in terms of reaction coordinates presents a daunting challenge. Here, our newly developed energy landscape visualization method (ELViM), a reaction coordinate-free approach, shows its prime application to explore frustrated energy landscapes of an intrinsically disordered protein, prostate-associated gene 4 (PAGE4). PAGE4 is a transcriptional coactivator that potentiates the oncogene c-Jun. Two kinases, namely, HIPK1 and CLK2, phosphorylate PAGE4, generating variants phosphorylated at different serine/threonine residues (HIPK1-PAGE4 and CLK2-PAGE4, respectively) with opposing functions. While HIPK1-PAGE4 predominantly phosphorylates Thr51 and potentiates c-Jun, CLK2-PAGE4 hyperphosphorylates PAGE4 and attenuates transactivation. To understand the underlying mechanisms of conformational diversity among different phosphoforms, we have analyzed their atomistic trajectories simulated using AWSEM forcefield, and the energy landscapes were elucidated using ELViM. This method allows us to identify and compare the population distributions of different conformational ensembles of PAGE4 phosphoforms using the same effective phase space. The results reveal a predominant conformational ensemble with an extended C-terminal segment of WT PAGE4, which exposes a functional residue Thr51, implying its potential of undertaking a fly-casting mechanism while binding to its cognate partner. In contrast, for HIPK1-PAGE4, a compact conformational ensemble enhances its population sequestering phosphorylated-Thr51. This clearly explains the experimentally observed weaker affinity of HIPK1-PAGE4 for c-Jun. ELViM appears as a powerful tool, especially to analyze the highly frustrated energy landscape representation of IDPs where appropriate reaction coordinates are hard to apprehend.

Structural Proteomics Methods to Interrogate the Conformations and Dynamics of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) and regions of intrinsic disorder (IDRs) are abundant in proteomes and are essential for many biological processes. Thus, they are often implicated in disease mechanisms, including neurodegeneration and cancer. The flexible nature of IDPs and IDRs provides many advantages, including (but not limited to) overcoming steric restrictions in binding, facilitating posttranslational modifications, and achieving high binding specificity with low affinity. IDPs adopt a heterogeneous structural ensemble, in contrast to typical folded proteins, making it challenging to interrogate their structure using conventional tools. Structural mass spectrometry (MS) methods are playing an increasingly important role in characterizing the structure and function of IDPs and IDRs, enabled by advances in the design of instrumentation and the development of new workflows, including in native MS, ion mobility MS, top-down MS, hydrogen-deuterium exchange MS, crosslinking MS, and covalent labeling. Here, we describe the advantages of these methods that make them ideal to study IDPs and highlight recent applications where these tools have underpinned new insights into IDP structure and function that would be difficult to elucidate using other methods.

APOD: accurate sequence-based predictor of disordered flexible linkers

Motivation

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

Results

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

Availability and implementation

https://yanglab.nankai.edu.cn/APOD/.

Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins

MOTIVATION

There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions).

RESULTS

Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs.

AVAILABILITY AND IMPLEMENTATION

HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell

Intrinsic disorder can be found in all proteomes of all kingdoms of life and in viruses, being particularly prevalent in the eukaryotes. We conduct a comprehensive analysis of the intrinsic disorder in the human proteins while mapping them into 24 compartments of the human cell. In agreement with previous studies, we show that human proteins are significantly enriched in disorder relative to a generic protein set that represents the protein universe. In fact, the fraction of proteins with long disordered regions and the average protein-level disorder content in the human proteome are about 3 times higher than in the protein universe. Furthermore, levels of intrinsic disorder in the majority of human subcellular compartments significantly exceed the average disorder content in the protein universe. Relative to the overall amount of disorder in the human proteome, proteins localized in the nucleus and cytoskeleton have significantly increased amounts of disorder, measured by both high disorder content and presence of multiple long intrinsically disordered regions. We empirically demonstrate that, on average, human proteins are assigned to 2.3 subcellular compartments, with proteins localized to few subcellular compartments being more disordered than the proteins that are localized to many compartments. Functionally, the disordered proteins localized in the most disorder-enriched subcellular compartments are primarily responsible for interactions with nucleic acids and protein partners. This is the first-time disorder is comprehensively mapped into the human cell. Our observations add a missing piece to the puzzle of functional disorder and its organization inside the cell.

Intrinsically disordered features of carbonic anhydrase IX proteoglycan-like domain

hCA IX is a multi-domain protein belonging to the family of hCAs which are ubiquitous zinc enzymes that catalyze the reversible hydration of CO2 to HCO3- and H+. hCA IX is a tumor-associated enzyme with a limited distribution in normal tissues, but over-expressed in many tumors, and is a promising drug target. Although many studies concerning the CA IX catalytic domain were performed, little is known about the proteoglycan-like (PG-like) domain of hCA IX which has been poorly investigated so far. Here we attempt to fill this gap by providing an overview on the functional, structural and therapeutic studies of the PG-like domain of hCA IX which represents a unique feature within the CA family. The main studies and recent advances concerning PG role in modulating hCA IX catalytic activity as well as in tumor spreading and migration are here reported. Special attention has been paid to the newly discovered disordered features of the PG domain which open new perspectives about its molecular mechanisms of action under physiological and pathological conditions, since disorder is likely involved in mediating interactions with partner proteins. The emerged disordered features of PG domain will be explored for putative diagnostic and therapeutic applications involving CA IX targeting in tumors.

Intrinsic disorder in protein domains contributes to both organism complexity and clade-specific functions

Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it’s greater for domains among different proteins than those within the same proteins. Some clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs.

Bioinformatics-based Identification of Proteins Expressed by Arthropod- borne Viruses Transmitted by Aedes Aegypti Mosquito

Background:

The female Aedes aegypti mosquito is a vector of several arthropod-borne viruses, such as Mayaro, Dengue, Chikungunya, Yellow Fever, and Zika. These viruses cause the death of at least 600000 people a year and temporarily disable several million more around the world. Up to date, there are no effective prophylactic measures that would prevent the contact and bite of this arthropod and, therefore, its consequential contagion.

Objective:

The objective of the present study was to search for the regularities of the proteins expressed by these five viruses, at residues level, and obtain a “bioinformatic fingerprint” to select them.

Methods:

We used two bioinformatic systems, our in-house bioinformatic system named Polarity Index Method® (PIM®) supported at residues level, and the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. We applied both programs to the 29 proteins that express the five groups of arboviruses studied, and we calculated for each of them their Polarity Index Method® profile and their intrinsic disorder predisposition. This information was then compared with analogous information for other protein groups, such as proteins from bacteria, fungi, viruses, and cell-penetrating peptides from the UniProt database, and a set of intrinsically disordered proteins. Once the “fingerprint” of each group of arboviruses was obtained, these “fingerprints” were searched among the 559228 “reviewed” proteins from the UniProt database.

Results:

In total, 1736 proteins were identified from the 559228 “reviewed” proteins from the UniProt database, with similar “PIM® profile” to the 29 mutated proteins that express the five groups of arboviruses.

Conclusion:

We propose that the “PIM® profile” of characterization of proteins might be useful for the identification of proteins expressed by arthropod-borne viruses transmitted by Aedes aegypti mosquito.

Recent Developments in the Field of Intrinsically Disordered Proteins: Intrinsic Disorder-Based Emergence in Cellular Biology in Light of the Physiological and Pathological Liquid-Liquid Phase Transitions

This review deals with two important concepts-protein intrinsic disorder and proteinaceous membrane-less organelles (PMLOs). The past 20 years have seen an upsurge of scientific interest in these phenomena. However, neither are new discoveries made in this century, but instead are timely reincarnations of old ideas that were mostly ignored by the scientific community for a long time. Merging these concepts in the form of the intrinsic disorder-based biological liquid-liquid phase separation provides a basis for understanding the molecular mechanisms of PMLO biogenesis.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

DescribePROT: database of amino acid-level protein structure and function predictions

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Functional characterization of an unknown soybean intrinsically disordered protein in vitro and in Escherichia coli

Intrinsically disordered proteins (IDPs) possess a wide range of biological function in all organisms, however the specific functions of most IDPs are still unknown. Soybean LOC protein, LOC for short, is a heat-stable protein, which is more abundant in the stress-resistant radicles. Sequence alignment and phylogenetic analysis showed that LOC is a functionally unknown protein and conserved in Fabaceae. LOC, being enriched in most disorder-promoting residues and depleted in most order-promoting residues, was predicted to contain high levels of intrinsic disorder by several commonly used computational tools. However, it was also predicted to contain two disorder-based protein-protein binding sites and two short α-helical segments. The circular dichroism spectroscopic analysis showed that this protein is mostly disordered in water, but can form more α-helical structure in the presence of SDS and TFE. Functional in vitro studies showed that the LOC protein is able to prevent lactate dehydrogenase inactivation by freeze-thaw at a molar ratio of 10:1. Furthermore, in vivo analyses revealed the survival rate of Escherichia coli over-expressing LOC protein under the conditions of osmotic stress was noticeably increased in comparison with the control. These observations suggest that the intrinsically disordered protein LOC might serve as a chaperone and/or cell protector.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

The complexity of protein interactions unravelled from structural disorder

The importance of unstructured biology has quickly grown during the last decades accompanying the explosion of the number of experimentally resolved protein structures. The idea that structural disorder might be a novel mechanism of protein interaction is widespread in the literature, although the number of statistically significant structural studies supporting this idea is surprisingly low. At variance with previous works, our conclusions rely exclusively on a large-scale analysis of all the 134337 X-ray crystallographic structures of the Protein Data Bank averaged over clusters of almost identical protein sequences. In this work, we explore the complexity of the organisation of all the interaction interfaces observed when a protein lies in alternative complexes, showing that interfaces progressively add up in a hierarchical way, which is reflected in a logarithmic law for the size of the union of the interface regions on the number of distinct interfaces. We further investigate the connection of this complexity with different measures of structural disorder: the standard missing residues and a new definition, called "soft disorder", that covers all the flexible and structurally amorphous residues of a protein. We show evidences that both the interaction interfaces and the soft disordered regions tend to involve roughly the same amino-acids of the protein, and preliminary results suggesting that soft disorder spots those surface regions where new interfaces are progressively accommodated by complex formation. In fact, our results suggest that structurally disordered regions not only carry crucial information about the location of alternative interfaces within complexes, but also about the order of the assembly. We verify these hypotheses in several examples, such as the DNA binding domains of P53 and P73, the C3 exoenzyme, and two known biological orders of assembly. We finally compare our measures of structural disorder with several disorder bioinformatics predictors, showing that these latter are optimised to predict the residues that are missing in all the alternative structures of a protein and they are not able to catch the progressive evolution of the disordered regions upon complex formation. Yet, the predicted residues, when not missing, tend to be characterised as soft disordered regions.

Towards an understanding of the role of intrinsic protein disorder on plant adaptation to environmental challenges

Intrinsic protein disorder is an interesting structural feature where fully functional proteins lack a three-dimensional structure in solution. In this work, we estimated the relative content of intrinsic protein disorder in 96 plant proteomes including monocots and eudicots. In this analysis, we found variation in the relative abundance of intrinsic protein disorder among these major clades; the relative level of disorder is higher in monocots than eudicots. In turn, there is an inverse relationship between the degree of intrinsic protein disorder and protein length, with smaller proteins being more disordered. The relative abundance of amino acids depends on intrinsic disorder and also varies among clades. Within the nucleus, intrinsically disordered proteins are more abundant than ordered proteins. Intrinsically disordered proteins are specialized in regulatory functions, nucleic acid binding, RNA processing, and in response to environmental stimuli. The implications of this on plants' responses to their environment are discussed.

Assessing the low complexity of protein sequences via the low complexity triangle

Background

Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat.

Results

We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest.

Conclusions

The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes.

Exploring Potential Signals of Selection for Disordered Residues in Prokaryotic and Eukaryotic Proteins

Intrinsically disordered proteins (IDPs) are an important class of proteins in all domains of life for their functional importance. However, how nature has shaped the disorder potential of prokaryotic and eukaryotic proteins is still not clearly known. Randomly generated sequences are free of any selective constraints, thus these sequences are commonly used as null models. Considering different types of random protein models, here we seek to understand how the disorder potential of natural eukaryotic and prokaryotic proteins differs from random sequences. Comparing proteome-wide disorder content between real and random sequences of 12 model organisms, we noticed that eukaryotic proteins are enriched in disordered regions compared to random sequences, but in prokaryotes such regions are depleted. By analyzing the position-wise disorder profile, we show that there is a generally higher disorder near the N- and C-terminal regions of eukaryotic proteins as compared to the random models; however, either no or a weak such trend was found in prokaryotic proteins. Moreover, here we show that this preference is not caused by the amino acid or nucleotide composition at the respective sites. Instead, these regions were found to be endowed with a higher fraction of protein–protein binding sites, suggesting their functional importance. We discuss several possible explanations for this pattern, such as improving the efficiency of protein–protein interaction, ribosome movement during translation, and post-translational modification. However, further studies are needed to clearly understand the biophysical mechanisms causing the trend.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

Bioinformatics analysis of correlation between protein function and intrinsic disorder

The correlation of molecular function and protein intrinsic disorder is an important aspect of understanding the relationship between function, sequence and structure. This research was inspired by statistical correlation evaluation method described by Xie et al. (J Proteome Res 6 (2007) 1882-1898, reference study), where the authors analyzed the relationship between structure and function of proteins from Swiss-Prot database and where these functions were described with Swiss-Prot function keywords. In this research, we investigated whether the conclusions from the reference study stand for another dataset with richer functional annotation. We used CAFA3 challenge training dataset where the function was described with terms from Gene Ontology (GO terms). In order to compare the results with the previous work, we associated the GO terms with the corresponding Swiss-Prot function keywords. The results were compared with the reference study by first repeating the analysis with Swiss-Prot function keywords and then by GO terms. We used PONDR VSL2b disorder predictor to label over 66,000 CAFA3 proteins as putatively disordered or ordered. Out of 186 Swiss-Prot keywords (belonging to molecular function type) with more than 20 annotated proteins, we found 47 to be highly order related and 44 highly disorder related. Using the same dataset and annotation constraints, out of 1781 GO term (belonging to molecular function type), we found 746 to be highly order related and 564 highly disorder related. GO term results are presented as interactive graphs displaying complex hierarchical structure of Gene Ontology. Comparison of two functional annotations, GO and Swiss-Prot keywords, showed consistent results in cases when it was possible to map a Swiss-Prot keyword to a corresponding GO term. Because of the small number of such cases, we propose a new method for deriving the missing mappings between Swiss-Prot keywords and GO terms with the highest likelihood by measuring similarity (Jaccard index) between sets of protein annotated by different functions. Comparison with results from the reference study revealed prevalence of binding related functions (disorder related) in the current dataset even though the same functions were not present in previous results.

Liquid-Liquid Phase Separation by Intrinsically Disordered Protein Regions of Viruses: Roles in Viral Life Cycle and Control of Virus-Host Interactions

Intrinsically disordered proteins (IDPs) are unable to adopt a unique 3D structure under physiological conditions and thus exist as highly dynamic conformational ensembles. IDPs are ubiquitous and widely spread in the protein realm. In the last decade, compelling experimental evidence has been gathered, pointing to the ability of IDPs and intrinsically disordered regions (IDRs) to undergo liquid-liquid phase separation (LLPS), a phenomenon driving the formation of membrane-less organelles (MLOs). These biological condensates play a critical role in the spatio-temporal organization of the cell, where they exert a multitude of key biological functions, ranging from transcriptional regulation and silencing to control of signal transduction networks. After introducing IDPs and LLPS, we herein survey available data on LLPS by IDPs/IDRs of viral origin and discuss their functional implications. We distinguish LLPS associated with viral replication and trafficking of viral components, from the LLPS-mediated interference of viruses with host cell functions. We discuss emerging evidence on the ability of plant virus proteins to interfere with the regulation of MLOs of the host and propose that bacteriophages can interfere with bacterial LLPS, as well. We conclude by discussing how LLPS could be targeted to treat phase separation-associated diseases, including viral infections.

Functions of short lifetime biological structures at large: the case of intrinsically disordered proteins

Although for more than a century a protein function was intimately associated with the presence of unique structure in a protein molecule, recent years witnessed a skyrocket rise of the appreciation of protein intrinsic disorder concept that emphasizes the importance of the biologically active proteins without ordered structures. In different proteins, the depth and breadth of disorder penetrance are different, generating an amusing spatiotemporal heterogeneity of intrinsically disordered proteins (IDPs) and intrinsically disordered protein region regions (IDPRs), which are typically described as highly dynamic ensembles of rapidly interconverting conformations (or a multitude of short lifetime structures). IDPs/IDPRs constitute a substantial part of protein kingdom and have unique functions complementary to functional repertoires of ordered proteins. They are recognized as interaction specialists and global controllers that play crucial roles in regulation of functions of their binding partners and in controlling large biological networks. IDPs/IDPRs are characterized by immense binding promiscuity and are able to use a broad spectrum of binding modes, often resulting in the formation of short lifetime complexes. In their turn, functions of IDPs and IDPRs are controlled by various means, such as numerous posttranslational modifications and alternative splicing. Some of the functions of IDPs/IDPRs are briefly considered in this review to shed some light on the biological roles of short-lived structures at large.

Bioinformatic Analysis and Biophysical Characterization Reveal Structural Disorder in G0S2 Protein

G0S2 is a small protein of 103 residues in length that is involved in multiple cellular processes. To date, several reports have shown that G0S2 functions by making direct protein-protein interactions with key proteins. In lipolysis, G0S2 specifically interacts with adipose triglyceride lipase, inhibiting its activity and resulting in lipolysis being downregulated. In a similar way, G0S2 also participates in the regulation of apoptosis, cell proliferation, and oxidative phosphorylation; however, information regarding G0S2 structural and biophysical properties is limited. In this work, we conducted a comparative structural analysis of human and mouse G0S2 proteins. Bioinformatics suggests the presence of a disordered C-terminal region in human G0S2. Experimental characterization by size-exclusion chromatography and dynamic light scattering showed that human and mouse G0S2 have different hydrodynamic properties. In comparison to the mouse G0S2, which behaves similar to a globular protein, the human G0S2 shows an elongated conformation, most likely by displaying a disordered C-terminal region. Further analysis of hydrodynamic properties under denaturing conditions suggests the presence of a structural element in the mouse protein that undergoes an order to disorder transition at low urea concentration. Structural analysis by circular dichroism revealed that in native conditions, both proteins are mainly unstructured, showing the presence of beta sheet structures. Further analysis of CD data suggests that both proteins belong to the premolten globule family of intrinsically disordered proteins. We suggest that the intrinsic disorder observed in the G0S2 protein may facilitate its interaction with multiple partners in the regulation of cellular metabolism.

Evolutionary Study of Disorder in Protein Sequences

Intrinsically disordered proteins (IDPs) contain regions lacking intrinsic globular structure (intrinsically disordered regions, IDRs). IDPs are present across the tree of life, with great variability of IDR type and frequency even between closely related taxa. To investigate the function of IDRs, we evaluated and compared the distribution of disorder content in 10,695 reference proteomes, confirming its high variability and finding certain correlation along the Euteleostomi (bony vertebrates) lineage to number of cell types. We used the comparison of orthologs to study the function of disorder related to increase in cell types, observing that multiple interacting subunits of protein complexes might gain IDRs in evolution, thus stressing the function of IDRs in modulating protein-protein interactions, particularly in the cell nucleus. Interestingly, the conservation of local compositional biases of IDPs follows residue-type specific patterns, with E- and K-rich regions being evolutionarily stable and Q- and A-rich regions being more dynamic. We provide a framework for targeted evolutionary studies of the emergence of IDRs. We believe that, given the large variability of IDR distributions in different species, studies using this evolutionary perspective are required.

The dark side of Alzheimer's disease: unstructured biology of proteins from the amyloid cascade signaling pathway

Alzheimer's disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure-function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

Intrinsic Disorder in Human Proteins Encoded by Core Duplicon Gene Families

Segmental duplications (i.e., highly homologous DNA fragments greater than 1 kb in length that are present within a genome at more than one site) are typically found in genome regions that are prone to rearrangements. A noticeable fraction of the human genome (∼5%) includes segmental duplications (or duplicons) that are assumed to play a number of vital roles in human evolution, human-specific adaptation, and genomic instability. Despite their importance for crucial events such as synaptogenesis, neuronal migration, and neocortical expansion, these segmental duplications continue to be rather poorly characterized. Of particular interest are the core duplicon gene (CDG) families, which are replicates sharing common "core" DNA among the randomly attached pieces and which expand along single chromosomes and might harbor newly acquired protein domains. Another important feature of proteins encoded by CDG families is their multifunctionality. Although it seems that these proteins might possess many characteristic features of intrinsically disordered proteins, to the best of our knowledge, a systematic investigation of the intrinsic disorder predisposition of the proteins encoded by core duplicon gene families has not been conducted yet. To fill this gap and to determine the degree to which these proteins might be affected by intrinsic disorder, we analyzed a set of human proteins encoded by the members of 10 core duplicon gene families, such as NBPF, RGPD, GUSBP, PMS2P, SPATA31, TRIM51, GOLGA8, NPIP, TBC1D3, and LRRC37. Our analysis revealed that the vast majority of these proteins are highly disordered, with their disordered regions often being utilized as means for the protein-protein interactions and/or targeted for numerous posttranslational modifications of different nature.

Identification of Intrinsic Disorder in Complexes from the Protein Data Bank

Background: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown. Results: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding. Conclusions: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure–function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function.

Diversity and genome mapping assessment of disordered and functional domains in trypanosomatids

The proteins that have structural disorder exemplify a class of proteins which is part of a new frontier in structural biology that demands a new understanding of the paradigm of structure/function correlations. In order to address the location, relative distances and the functional/structural correlation between disordered and conserved domains, consensus disordered predictions were mapped together with CDD domains in Leishmania braziliensis M2904, Leishmania infantum JPCM5, Trypanosoma cruzi CL-Brener Esmeraldo-like, Trypanosoma cruzi Dm28c, Trypanosoma cruzi Sylvio X10, Blechomonas ayalai B08-376 and Paratrypanosoma confusum CUL13 predicted proteomes. Our results depicts the role of protein disorder in key aspects of parasites biology highlighting: a) statistical significant association between genome structural location of protein disordered consensus stretches and functional domains; b) that disordered protein stretches appear in greater percentage at upstream or downstream position of the predicted domain; c) a possible role of structural disorder in several gene expression, control points that includes but are not limited to: i) protein folding; ii) protein transport and degradation; and iii) protein modification. In addition, for values of protein with disorder content greater than 40%, a small percentage of protein binding sites in IDPs/IDRs, a higher hypothetical protein annotation frequency was observed than expected by chance and trypanosomatid multigene families linked with virulence are rich in protein with disorder content. SIGNIFICANCE: T. cruzi and Leishmania spp are the etiological agents of Chagas disease and leishmaniasis, respectively. Currently, no vaccine or effective drug treatment is available against these neglected diseases and the knowledge about the post-transcriptional and post-translational mechanisms of these organisms, which are key for this scenario, remain scarce. This study depicts the potential impact of the proximity between protein structural disorder and functional domains in the post-transcriptional regulation of pathogenic versus human non-pathogenic trypanosomatids. Our results revealed a significant statistical relationship between the genome structural locations of these two variables and disordered regions appearing more frequently at upstream or downstream positions of the CDD locus domain. This flexibility feature would maintain structural accessibility of functional sites for post-translational modifications, shedding light into this important aspect of parasite biology. This hypothesis is corroborated by the functional enrichment analysis of disordered proteins subset that highlight the involvement of this class of proteins in protein folding, protein transport and degradation and protein modification. Furthermore, our results pointed out: a) the impact of protein disorder in the process of genome annotation (proteins tend to be annotated as hypothetical when the disorder content reaches ~40%); b) that trypanosomatid multigenic families linked with virulence have a key protein disorder content.

Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet

Intrinsically-disordered protein (IDP) characterization was an amazing change of paradigm in our classical sequence-structure-function theory. Moreover, IDPs are over-represented in major disease pathways and are now often targeted using small molecules for therapeutic purposes. This has had created a complex continuum from order-that encompasses rigid and flexible regions-to disorder regions; the latter being not accessible through classical crystallographic methodologies. In X-ray structures, the notion of order is dictated by access to resolved atom positions, providing rigidity and flexibility information with low and high experimental B-factors, while disorder is associated with the missing (non-resolved) residues. Nonetheless, some rigid regions can be found in disorder regions. Using ensembles of IDPs, their local conformations were analyzed in the light of a structural alphabet. An entropy index derived from this structural alphabet allowed us to propose a continuum of states from rigidity to flexibility and finally disorder. In this study, the analysis was extended to comparing these results to disorder predictions, underlying a limited correlation, and so opening new ideas to characterize and predict disorder.