Roles of intrinsic disorder in protein-nucleic acid interactions

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins

Current predictors of DNA-binding residues (DBRs) from protein sequences belong to two distinct groups, those trained on binding annotations extracted from structured protein-DNA complexes (structure-trained) vs. intrinsically disordered proteins (disorder-trained). We complete the first empirical analysis of predictive performance across the structure- and disorder-annotated proteins for a representative collection of ten predictors. Majority of the structure-trained tools perform well on the structure-annotated proteins while doing relatively poorly on the disorder-annotated proteins, and vice versa. Several methods make accurate predictions for the structure-annotated proteins or the disorder-annotated proteins, but none performs highly accurately for both annotation types. Moreover, most predictors make excessive cross-predictions for the disorder-annotated proteins, where residues that interact with non-DNA ligand types are predicted as DBRs. Motivated by these results, we design, validate and deploy an innovative meta-model, hybridDBRpred, that uses deep transformer network to combine predictions generated by three best current predictors. HybridDBRpred provides accurate predictions and low levels of cross-predictions across the two annotation types, and is statistically more accurate than each of the ten tools and baseline meta-predictors that rely on averaging and logistic regression. We deploy hybridDBRpred as a convenient web server at http://biomine.cs.vcu.edu/servers/hybridDBRpred/ and provide the corresponding source code at https://github.com/jianzhang-xynu/hybridDBRpred.

Acetylation of nuclear receptors in health and disease: an update

Lysine acetylation is a common reversible post-translational modification of proteins that plays a key role in regulating gene expression. Nuclear receptors (NRs) include ligand-inducible transcription factors and orphan receptors for which the ligand is undetermined, which together regulate the expression of genes involved in development, metabolism, homeostasis, reproduction and human diseases including cancer. Since the original finding that the ERα, AR and HNF4 are acetylated, we now understand that the vast majority of NRs are acetylated and that this modification has profound effects on NR function. Acetylation sites are often conserved and involve both ordered and disordered regions of NRs. The acetylated residues function as part of an intramolecular signalling platform intersecting phosphorylation, methylation and other modifications. Acetylation of NR has been shown to impact recruitment into chromatin, co-repressor and coactivator complex formation, sensitivity and specificity of regulation by ligand and ligand antagonists, DNA binding, subcellular distribution and transcriptional activity. A growing body of evidence in mice indicates a vital role for NR acetylation in metabolism. Additionally, mutations of the NR acetylation site occur in human disease. This review focuses on the role of NR acetylation in coordinating signalling in normal physiology and disease.

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

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

Driving forces of the complex formation between highly charged disordered proteins

Highly disordered complexes between oppositely charged intrinsically disordered proteins present a new paradigm of biomolecular interactions. Here, we investigate the driving forces of such interactions for the example of the highly positively charged linker histone H1 and its highly negatively charged chaperone, prothymosin α (ProTα). Temperature-dependent single-molecule Förster resonance energy transfer (FRET) experiments and isothermal titration calorimetry reveal ProTα-H1 binding to be enthalpically unfavorable, and salt-dependent affinity measurements suggest counterion release entropy to be an important thermodynamic driving force. Using single-molecule FRET, we also identify ternary complexes between ProTα and H1 in addition to the heterodimer at equilibrium and show how they contribute to the thermodynamics observed in ensemble experiments. Finally, we explain the observed thermodynamics quantitatively with a mean-field polyelectrolyte theory that treats counterion release explicitly. ProTα-H1 complex formation resembles the interactions between synthetic polyelectrolytes, and the underlying principles are likely to be of broad relevance for interactions between charged biomolecules in general.

Multiple domains of the integral KREPA3 protein are critical for the structure and precise functions of RNA editing catalytic complexes in Trypanosoma brucei

The gRNA directed U-insertion and deletion editing of mitochondrial mRNAs that is essential in different life-cycle stages for the protozoan parasite Trypanosoma brucei is performed by three similar multiprotein catalytic complexes (CCs) that contain the requisite enzymes. These CCs also contain a common set of eight proteins that have no apparent direct catalytic function, including six that have an OB-fold domain. We show here that one of these OB-fold proteins, KREPA3 (A3), has structural homology to other editing proteins, is essential for editing, and is multifunctional. We investigated A3 function by analyzing the effects of single amino acid loss of function mutations, most of which were identified by screening bloodstream form (BF) parasites for loss of growth following random mutagenesis. Mutations in the zinc fingers (ZFs), an intrinsically disordered region (IDR), and several within or near the carboxy-terminal OB-fold domain variably impacted CC structural integrity and editing. Some mutations resulted in almost complete loss of CCs and its proteins and editing, whereas others retained CCs but had aberrant editing. All but a mutation which is near the OB-fold affected growth and editing in BF but not procyclic form (PF) parasites. These data indicate that multiple positions within A3 have essential functions that contribute to the structural integrity of CCs, the precision of editing and the developmental differences in editing between BF and PF stages.

Influence of HIV-1 Genomic RNA on the Formation of Gag Biomolecular Condensates

Biomolecular condensates (BMCs) play an important role in the replication of a growing number of viruses, but many important mechanistic details remain to be elucidated. Previously, we demonstrated that the pan-retroviral nucleocapsid (NC) and HIV-1 pr55Gag (Gag) proteins phase separate into condensates, and that HIV-1 protease (PR)-mediated maturation of Gag and Gag-Pol precursor proteins yields self-assembling BMCs that have HIV-1 core architecture. Using biochemical and imaging techniques, we aimed to further characterize the phase separation of HIV-1 Gag by determining which of its intrinsically disordered regions (IDRs) influence the formation of BMCs, and how the HIV-1 viral genomic RNA (gRNA) could influence BMC abundance and size. We found that mutations in the Gag matrix (MA) domain or the NC zinc finger motifs altered condensate number and size in a salt-dependent manner. Gag BMCs were also bimodally influenced by the gRNA, with a condensate-promoting regime at lower protein concentrations and a gel dissolution at higher protein concentrations. Interestingly, incubation of Gag with CD4+ T cell nuclear lysates led to the formation of larger BMCs compared to much smaller ones observed in the presence of cytoplasmic lysates. These findings suggest that the composition and properties of Gag-containing BMCs may be altered by differential association of host factors in nuclear and cytosolic compartments during virus assembly. This study significantly advances our understanding of HIV-1 Gag BMC formation and provides a foundation for future therapeutic targeting of virion assembly.

Arginine anchor points govern H3 tail dynamics

Chromatin is dynamically reorganized spatially and temporally, and the post-translational modification of histones is a key component of this regulation. The basic subunit of chromatin is the nucleosome core particle, consisting of two copies each of the histones H2A, H2B, H3, and H4 around which ∼147 base pairs of DNA wrap. The intrinsically disordered histone termini, or tails, protrude from the core and are heavily post-translationally modified. Previous studies have shown that the histone tails exist in dynamic ensembles of DNA-bound states within the nucleosome. Histone tail interactions with DNA are involved in nucleosome conformation and chromatin organization. Charge-modulating histone post-translational modifications (PTMs) are poised to perturb the dynamic interactions between histone tails and DNA. Arginine side chains form favorable interactions with DNA and are sites of charge-modulating PTMs such as citrullination. Our current focus is on the H3 tail, the longest histone tail. Four arginine residues are relatively evenly spaced along the H3 tail sequence, suggesting multivalent interactions with DNA poised for regulation by PTMs. In this study, we use NMR nuclear spin relaxation experiments to investigate the contribution of arginine residues to H3 tail dynamics within the nucleosome core particle. By neutralizing arginine via mutation to glutamine, we begin to work towards a comprehensive understanding of the contribution of individual residues to H3 tail dynamics. We find that neutralization of arginine residues results in increased regional mobility of the H3 tails, with implications for understanding the direct effects of arginine citrullination. Altogether, these studies support a role for dynamics within the histone language and emphasize the importance of charge-modulating histone PTMs in regulating chromatin dynamics, starting at the level of the basic subunit of chromatin.

Multiple domains of the integral KREPA3 protein are critical for the structure and precise functions of RNA Editing Catalytic Complexes in Trypanosoma brucei

The gRNA directed U-insertion and deletion editing of mitochondrial mRNAs that is essential in different life cycle stages for the protozoan parasite Trypanosoma brucei is performed by three similar multi-protein catalytic complexes (CCs) that contain the requisite enzymes. These CCs also contain a common set of eight proteins that have no apparent direct catalytic function, including six that have an OB-fold domain. We show here that one of these OB-fold proteins, KREPA3 (A3), has structural homology to other editing proteins, is essential for editing and is multifunctional. We investigated A3 function by analyzing the effects of single amino acid loss of function mutations most of which were identified by screening bloodstream form (BF) parasites for loss of growth following random mutagenesis. Mutations in the ZFs, an intrinsically disordered region (IDR) and several within or near the C-terminal OB-fold domain variably impacted CC structural integrity and editing. Some mutations resulted in almost complete loss of CCs and its proteins and editing whereas others retained CCs but had aberrant editing. All but a mutation which is near the OB-fold affected growth and editing in BF but not procyclic form (PF) parasites. These data indicate that multiple positions within A3 have essential functions that contribute to the structural integrity of CCs, the precision of editing and the developmental differences in editing between BF and PF stages.

Computational prediction of disordered binding regions

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

Influence of HIV-1 genomic RNA on the formation of Gag biomolecular condensates

Biomolecular condensates (BMCs) play an important role in the replication of a growing number of viruses, but many important mechanistic details remain to be elucidated. Previously, we demonstrated that pan-retroviral nucleocapsid (NC) and the HIV-1 pr55 ^Gag (Gag) proteins phase separate into condensates, and that HIV-1 protease (PR)-mediated maturation of Gag and Gag-Pol precursor proteins yield self-assembling BMCs having HIV-1 core architecture. Using biochemical and imaging techniques, we aimed to further characterize the phase separation of HIV-1 Gag by determining which of its intrinsically disordered regions (IDRs) influence the formation of BMCs and how the HIV-1 viral genomic RNA (gRNA) could influence BMC abundance and size. We found that mutations in the Gag matrix (MA) domain or the NC zinc finger motifs altered condensate number and size in a salt-dependent manner. Gag BMCs were also bimodally influenced by the gRNA, with a condensate-promoting regime at lower protein concentrations and a gel dissolution at higher protein concentrations. Interestingly, incubation of Gag with CD4 ⁺ T cell nuclear lysates led to the formation of larger BMCs as compared to much smaller ones observed in the presence of cytoplasmic lysates. These findings suggests that the composition and properties of Gag-containing BMCs may be altered by differential association of host factors in nuclear and cytosolic compartments during virus assembly. This study significantly advances our understanding of HIV-1 Gag BMC formation and provides a foundation for future therapeutic targeting of virion assembly.

Compact Cas9d and HEARO enzymes for genome editing discovered from uncultivated microbes

Programmable, RNA-guided nucleases are diverse enzymes that have been repurposed for biotechnological applications. However, to further expand the therapeutic application of these tools there is a need for targetable systems that are small enough to be delivered efficiently. Here, we mined an extensive genome-resolved metagenomics database and identified families of uncharacterized RNA-guided, compact nucleases (between 450 and 1,050 aa). We report that Cas9d, a new CRISPR type II subtype, contains Zinc-finger motifs and high arginine content, features that we also found in nucleases related to HEARO effectors. These enzymes exhibit diverse biochemical characteristics and are broadly targetable. We show that natural Cas9d enzymes are capable of genome editing in mammalian cells with >90% efficiency, and further engineered nickase variants into the smallest base editors active in E. coli and human cells. Their small size, broad targeting potential, and translatability suggest that Cas9d and HEARO systems will enable a variety of genome editing applications.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Alternative Mechanisms for DNA Engagement by BET Bromodomain-Containing Proteins

Epigenetic reader domains regulate chromatin structure and modulate gene expression through the recognition of post-translational modifications on histones. Recently, reader domains have also been found to harbor double-stranded (ds) DNA-binding activity, which is as functionally critical as histone association. Here, we explore the dsDNA recognition of the N-terminal bromodomain of the bromodomain and extra-terminal (BET) protein, BRD4. Using protein-observed ¹⁹F NMR, ¹H-¹⁵N HSQC NMR, electrophoretic mobility shift assays (EMSA), and competitive-inhibition assays, we establish the binding surface of dsDNA and find it to be largely overlapping with the acetylated histone (KAc)-binding site. Rather than engaging in electrostatic contacts, we find dsDNA to interact competitively within the KAc-binding pocket. These interactions are distinct from the highly homologous BET bromodomain, BRDT. Nine additional bromodomains have also been characterized for interacting with dsDNA, including tandem BET bromodomains. Together, these studies help establish a binding model for dsDNA interactions with BRD4 bromodomains and elucidate the chromatin-recognition mechanisms of the BRD4 protein for regulating gene expression.

Complementarity of the residue-level protein function and structure predictions in human proteins

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.

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/.

Highly accurate protein structure prediction for the human proteome

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

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.

Nucleosome composition regulates the histone H3 tail conformational ensemble and accessibility

Hexasomes and tetrasomes are intermediates in nucleosome assembly and disassembly. Their formation is promoted by histone chaperones, ATP-dependent remodelers, and RNA polymerase II. In addition, hexasomes are maintained in transcribed genes and could be an important regulatory factor. While nucleosome composition has been shown to affect the structure and accessibility of DNA, its influence on histone tails is largely unknown. Here, we investigate the conformational dynamics of the H3 tail in the hexasome and tetrasome. Using a combination of NMR spectroscopy, MD simulations, and trypsin proteolysis, we find that the conformational ensemble of the H3 tail is regulated by nucleosome composition. As has been found for the nucleosome, the H3 tails bind robustly to DNA within the hexasome and tetrasome, but upon loss of the H2A/H2B dimer, we determined that the adjacent H3 tail has an altered conformational ensemble, increase in dynamics, and increase in accessibility. Similar to observations of DNA dynamics, this is seen to be asymmetric in the hexasome. Our results indicate that nucleosome composition has the potential to regulate chromatin signaling and ultimately help shape the chromatin landscape.

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.

Characterization of functional disordered regions within chromatin-associated proteins

Intrinsically disordered regions (IDRs) are abundant and play important roles in the function of chromatin-associated proteins (CAPs). These regions are often found at the N- and C-termini of CAPs and between structured domains, where they can act as more than just linkers, directly contributing to function. IDRs have been shown to contribute to substrate binding, act as auto-regulatory regions, and drive liquid-liquid droplet formation. Their disordered nature provides increased functional diversity and allows them to be easily regulated through post-translational modification. However, these regions can be especially challenging to characterize on a structural level. Here, we review the prevalence of IDRs in CAPs, highlighting several studies that address their importance in CAP function and show progress in structural characterization of these regions. A focus is placed on the unique opportunity to apply nuclear magnetic resonance (NMR) spectroscopy alongside cryo-electron microscopy to characterize IDRs in CAPs.

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.

Zinc and Copper Ions Differentially Regulate Prion-Like Phase Separation Dynamics of Pan-Virus Nucleocapsid Biomolecular Condensates

Liquid-liquid phase separation (LLPS) is a rapidly growing research focus due to numerous demonstrations that many cellular proteins phase-separate to form biomolecular condensates (BMCs) that nucleate membraneless organelles (MLOs). A growing repertoire of mechanisms supporting BMC formation, composition, dynamics, and functions are becoming elucidated. BMCs are now appreciated as required for several steps of gene regulation, while their deregulation promotes pathological aggregates, such as stress granules (SGs) and insoluble irreversible plaques that are hallmarks of neurodegenerative diseases. Treatment of BMC-related diseases will greatly benefit from identification of therapeutics preventing pathological aggregates while sparing BMCs required for cellular functions. Numerous viruses that block SG assembly also utilize or engineer BMCs for their replication. While BMC formation first depends on prion-like disordered protein domains (PrLDs), metal ion-controlled RNA-binding domains (RBDs) also orchestrate their formation. Virus replication and viral genomic RNA (vRNA) packaging dynamics involving nucleocapsid (NC) proteins and their orthologs rely on Zinc (Zn) availability, while virus morphology and infectivity are negatively influenced by excess Copper (Cu). While virus infections modify physiological metal homeostasis towards an increased copper to zinc ratio (Cu/Zn), how and why they do this remains elusive. Following our recent finding that pan-retroviruses employ Zn for NC-mediated LLPS for virus assembly, we present a pan-virus bioinformatics and literature meta-analysis study identifying metal-based mechanisms linking virus-induced BMCs to neurodegenerative disease processes. We discover that conserved degree and placement of PrLDs juxtaposing metal-regulated RBDs are associated with disease-causing prion-like proteins and are common features of viral proteins responsible for virus capsid assembly and structure. Virus infections both modulate gene expression of metalloproteins and interfere with metal homeostasis, representing an additional virus strategy impeding physiological and cellular antiviral responses. Our analyses reveal that metal-coordinated virus NC protein PrLDs initiate LLPS that nucleate pan-virus assembly and contribute to their persistence as cell-free infectious aerosol droplets. Virus aerosol droplets and insoluble neurological disease aggregates should be eliminated by physiological or environmental metals that outcompete PrLD-bound metals. While environmental metals can control virus spreading via aerosol droplets, therapeutic interference with metals or metalloproteins represent additional attractive avenues against pan-virus infection and virus-exacerbated neurological diseases.

Japanese encephalitis virus - exploring the dark proteome and disorder-function paradigm

Japanese encephalitis virus (JEV) is one of the major causes of viral encephalitis all around the globe. Approximately 3 billion people in endemic areas are at risk of Japanese encephalitis. To develop a wholistic understanding of the viral proteome, it is important to investigate both its ordered and disordered proteins. However, the functional and structural significance of disordered regions in the JEV proteome has not been systematically investigated as of yet. To fill this gap, we used here a set of bioinformatics tools to analyze the JEV proteome for the predisposition of its proteins for intrinsic disorder and for the presence of the disorder-based binding regions (also known as molecular recognition features, MoRFs). We also analyzed all JEV proteins for the presence of the probable nucleic acid-binding (DNA and RNA) sites. The results of these computational studies are experimentally validated using JEV capsid protein as an illustrative example. In agreement with bioinformatic analysis, we found that the N-terminal region of the JEV capsid (residues 1-30) is intrinsically disordered. We showed that this region is characterized by the temperature response typical for highly disordered proteins. Furthermore, we have experimentally shown that this disordered N-terminal domain of a capsid protein has a noticeable 'gain-of-structure' potential. In addition, using DOPS liposomes, we demonstrated the presence of pronounced membrane-mediated conformational changes in the N-terminal region of JEV capsid. In our view, this disorder-centric analysis would be helpful for a better understanding of the JEV pathogenesis.

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quality assessment for the putative intrinsic disorder in proteins

MOTIVATION

While putative intrinsic disorder is widely used, none of the predictors provides quality assessment (QA) scores. QA scores estimate the likelihood that predictions are correct at a residue level and have been applied in other bioinformatics areas. We recently reported that QA scores derived from putative disorder propensities perform relatively poorly for native disordered residues. Here we design and validate a general approach to construct QA predictors for disorder predictions.

RESULTS

The QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions) toolbox of methods accommodates a diverse set of ten disorder predictors. It builds upon several innovative design elements including use and scaling of selected physicochemical properties of the input sequence, post-processing of disorder propensity scores, and a feature selection that optimizes the predictive models to a specific disorder predictor. We empirically establish that each one of these elements contributes to the overall predictive performance of our tool and that QUARTER's outputs significantly outperform QA scores derived from the outputs generated the disorder predictors. The best performing QA scores for a single disorder predictor identify 13% of residues that are predicted with 98% precision. QA scores computed by combining results of the ten disorder predictors cover 40% of residues with 95% precision. Case studies are used to show how to interpret the QA scores. QA scores based on the high precision combined predictions are applied to analyze disorder in the human proteome.

AVAILABILITY AND IMPLEMENTATION

http://biomine.cs.vcu.edu/servers/QUARTER/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intrinsically disordered proteins of viruses: Involvement in the mechanism of cell regulation and pathogenesis

Intrinsically disordered proteins (IDPs) possess the property of inherent flexibility and can be distinguished from other proteins in terms of lack of any fixed structure. Such dynamic behavior of IDPs earned the name "Dancing Proteins." The exploration of these dancing proteins in viruses has just started and crucial details such as correlation of rapid evolution, high rate of mutation and accumulation of disordered contents in viral proteome at least understood partially. In order to gain a complete understanding of this correlation, there is a need to decipher the complexity of viral mediated cell hijacking and pathogenesis in the host organism. Further there is necessity to identify the specific patterns within viral and host IDPs such as aggregation; Molecular recognition features (MoRFs) and their association to virulence, host range and rate of evolution of viruses in order to tackle the viral-mediated diseases. The current book chapter summarizes the aforementioned details and suggests the novel opportunities for further research of IDPs senses in viruses.

The phosphatase PAC1 acts as a T cell suppressor and attenuates host antitumor immunity

Cancer cells subvert immune surveillance through inhibition of T cell effector function. Elucidation of the mechanism of T cell dysfunction is therefore central to cancer immunotherapy. Here, we report that dual specificity phosphatase 2 (DUSP2; also known as phosphatase of activated cells 1, PAC1) acts as an immune checkpoint in T cell antitumor immunity. PAC1 is selectively upregulated in exhausted tumor-infiltrating lymphocytes and is associated with poor prognosis of patients with cancer. PAC1^hi effector T cells lose their proliferative and effector capacities and convert into exhausted T cells. Deletion of PAC1 enhances immune responses and reduces cancer susceptibility in mice. Through activation of EGR1, excessive reactive oxygen species in the tumor microenvironment induce expression of PAC1, which recruits the Mi-2β nucleosome-remodeling and histone-deacetylase complex, eventually leading to chromatin remodeling of effector T cells. Our study demonstrates that PAC1 is an epigenetic immune regulator and highlights the importance of targeting PAC1 in cancer immunotherapy.

Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor

Intrinsically disordered proteins are either entirely disordered or contain disordered regions in their native state. These proteins and regions function without the prerequisite of a stable structure and were found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behind the rapidly growing number of sequenced proteins, motivating the development of computational methods that predict disorder in protein sequences. DisCoP is a user-friendly webserver that provides accurate sequence-based prediction of protein disorder. It relies on meta-architecture in which the outputs generated by multiple disorder predictors are combined together to improve predictive performance. The architecture of disCoP is presented, and its accuracy relative to several other disorder predictors is briefly discussed. We describe usage of the web interface and explain how to access and read results generated by this computational tool. We also provide an example of prediction results and interpretation. The disCoP's webserver is publicly available at http://biomine.cs.vcu.edu/servers/disCoP/ .

Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER

Intrinsically disordered regions (IDRs) are estimated to be highly abundant in nature. While only several thousand proteins are annotated with experimentally derived IDRs, computational methods can be used to predict IDRs for the millions of currently uncharacterized protein chains. Several dozen disorder predictors were developed over the last few decades. While some of these methods provide accurate predictions, unavoidably they also make some mistakes. Consequently, one of the challenges facing users of these methods is how to decide which predictions can be trusted and which are likely incorrect. This practical problem can be solved using quality assessment (QA) scores that predict correctness of the underlying (disorder) predictions at a residue level. We motivate and describe a first-of-its-kind toolbox of QA methods, QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions), which provides the scores for a diverse set of ten disorder predictors. QUARTER is available to the end users as a free and convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTER/ . We briefly describe the predictive architecture of QUARTER and provide detailed instructions on how to use the webserver. We also explain how to interpret results produced by QUARTER with the help of a case study.

DISOselect: Disorder predictor selection at the protein level

The intense interest in the intrinsically disordered proteins in the life science community, together with the remarkable advancements in predictive technologies, have given rise to the development of a large number of computational predictors of intrinsic disorder from protein sequence. While the growing number of predictors is a positive trend, we have observed a considerable difference in predictive quality among predictors for individual proteins. Furthermore, variable predictor performance is often inconsistent between predictors for different proteins, and the predictor that shows the best predictive performance depends on the unique properties of each protein sequence. We propose a computational approach, DISOselect, to estimate the predictive performance of 12 selected predictors for individual proteins based on their unique sequence-derived properties. This estimation informs the users about the expected predictive quality for a selected disorder predictor and can be used to recommend methods that are likely to provide the best quality predictions. Our solution does not depend on the results of any disorder predictor; the estimations are made based solely on the protein sequence. Our solution significantly improves predictive performance, as judged with a test set of 1,000 proteins, when compared to other alternatives. We have empirically shown that by using the recommended methods the overall predictive performance for a given set of proteins can be improved by a statistically significant margin. DISOselect is freely available for non-commercial users through the webserver at http://biomine.cs.vcu.edu/servers/DISOselect/.

Computational prediction of functions of intrinsically disordered regions

Intrinsically disorder regions (IDRs) are abundant in nature, particularly among Eukaryotes. While they facilitate a wide spectrum of cellular functions including signaling, molecular assembly and recognition, translation, transcription and regulation, only several hundred IDRs are annotated functionally. This annotation gap motivates the development of fast and accurate computational methods that predict IDR functions directly from protein sequences. We introduce and describe a comprehensive collection of 25 methods that provide accurate predictions of IDRs that interact with proteins and nucleic acids, that function as flexible linkers and that moonlight multiple functions. Virtually all of these predictors can be accessed online and many were developed in the last few years. They utilize a wide range of predictive architectures and take advantage of modern machine learning algorithms. Our empirical analysis shows that predictors that are available as webservers enjoy high rates of citations, attesting to their practical value and popularity. The most cited methods include DISOPRED3, ANCHOR, alpha-MoRFpred, MoRFpred, fMoRFpred and MoRFCHiBi. We present two case studies to demonstrate that predictions produced by these computational tools are relatively easy to interpret and that they deliver valuable functional clues. However, the current computational tools cover a relatively narrow range of disorder functions. Further development efforts that would cover a broader range of functions should be pursued. We demonstrate that a sufficient amount of functionally annotated IDRs that are associated with several other disorder functions is already available and can be used to design and validate novel predictors.

A Molecular Dynamics Investigation of the Thermostability of Cold-Sensitive I707L KlenTaq1 DNA Polymerase and Its Wild-Type Counterpart

DNA polymerase I from Thermus aquaticus ( Taq DNA polymerase) is useful for polymerase chain reactions because of its exceptional thermostability; however, its activity at low temperatures can cause amplification of unintended products. Mutation of isoleucine 707 to leucine (I707L) slows Taq DNA polymerase at low temperatures, which decreases unwanted amplification due to mispriming. In this work, unrestrained molecular dynamics (MD) simulations were performed on I707L and wild-type (WT) Taq DNA polymerase at 341 and 298 K to determine how the mutation affects the dynamic nature of the protein. The results suggest that I707L Taq DNA polymerase remains relatively immobile at room temperature and becomes more flexible at the higher temperature, while the WT Taq DNA polymerase demonstrates less substantial differences in dynamics at high and low temperatures. These results are in agreement with previous experimental results on the I707L mutant Taq DNA polymerase that show dynamic differences at high and low temperatures. The decreased mobility of the mutant at low temperature suggests that the mutant remains longer in the blocked conformation, and this may lead to reduced activity relative to the WT at 298 K. Principal component analysis revealed that the mutation results in decoupled movements of the Q helix and fingers domain. This decoupled nature of the mutant gives way to an increasingly flexible N-terminal end of the Q helix at 341 K, a characteristic not seen for WT Taq DNA polymerase.

In Silico and In Vitro Considerations of Keratinocyte Nuclear Receptor Protein Structural Order for Improving Experimental Analysis

Nuclear receptors (NR) regulate gene expression critical in keratinocyte replication and differentiation. In addition to a ligand-binding domain, NR like other transcription factor families have a DNA-binding domain that must attain a particular conformation for effective interaction with the three-dimensional structure in promoters of target genes for control of their expression. Such protein-DNA assemblies extend the classic "lock and key" idea typified by protein-protein interactions. However, it is becoming increasingly clear that multi-subdomain transcription factors like NR frequently range along the length of the protein from structured, ordered regions expected for interaction with a preset partner to more flexible, intrinsically disordered regions which are more available for diverse posttranslational modifications and/or interaction with differing partners. The extended amino terminus of NR (the A/B subdomain) is one such intrinsically disordered region. Here we provide a primer on in silico-based recognition of amino acid composition and order associated with such conformational flexibility along with adaptations of readily accessible laboratory techniques (e.g., considerations for recombinant expression, sensitivity to protease and proteasome digestion) to facilitate initial prediction and testing for intrinsic disorder in various proteins of interest to keratinocyte biologists, like NR and other transcription factors.

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.

Large-Scale Analyses of Site-Specific Evolutionary Rates across Eukaryote Proteomes Reveal Confounding Interactions between Intrinsic Disorder, Secondary Structure, and Functional Domains

Various structural and functional constraints govern the evolution of protein sequences. As a result, the relative rates of amino acid replacement among sites within a protein can vary significantly. Previous large-scale work on Metazoan (Animal) protein sequence alignments indicated that amino acid replacement rates are partially driven by a complex interaction among three factors: intrinsic disorder propensity; secondary structure; and functional domain involvement. Here, we use sequence-based predictors to evaluate the effects of these factors on site-specific sequence evolutionary rates within four eukaryotic lineages: Metazoans; Plants; Saccharomycete Fungi; and Alveolate Protists. Our results show broad, consistent trends across all four Eukaryote groups. In all four lineages, there is a significant increase in amino acid replacement rates when comparing: (i) disordered vs. ordered sites; (ii) random coil sites vs. sites in secondary structures; and (iii) inter-domain linker sites vs. sites in functional domains. Additionally, within Metazoans, Plants, and Saccharomycetes, there is a strong confounding interaction between intrinsic disorder and secondary structure-alignment sites exhibiting both high disorder propensity and involvement in secondary structures have very low average rates of sequence evolution. Analysis of gene ontology (GO) terms revealed that in all four lineages, a high fraction of sequences containing these conserved, disordered-structured sites are involved in nucleic acid binding. We also observe notable differences in the statistical trends of Alveolates, where intrinsically disordered sites are more variable than in other Eukaryotes and the statistical interactions between disorder and other factors are less pronounced.

In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome

Deciphering a complete landscape of protein-RNA interactions in the human proteome remains an elusive challenge. We computationally elucidate RNA binding proteins (RBPs) using an approach that complements previous efforts. We employ two modern complementary sequence-based methods that provide accurate predictions from the structured and the intrinsically disordered sequences, even in the absence of sequence similarity to the known RBPs. We generate and analyze putative RNA binding residues on the whole proteome scale. Using a conservative setting that ensures low, 5% false positive rate, we identify 1511 putative RBPs that include 281 known RBPs and 166 RBPs that were previously predicted. We empirically demonstrate that these overlaps are statistically significant. We also validate the putative RBPs based on two major hallmarks of their RNA binding residues: high levels of evolutionary conservation and enrichment in charged amino acids. Moreover, we show that the novel RBPs are significantly under-annotated functionally which coincides with the fact that they were not yet found to interact with RNAs. We provide two examples of our novel putative RBPs for which there is recent evidence of their interactions with RNAs. The dataset of novel putative RBPs and RNA binding residues for the future hypothesis generation is provided in the Supporting Information.

Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins

Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein–RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids–nucleotide pairs; which showed the preference of His–G; Asn–U and Ser–U at for the interface of DOT regions. This study provides insights to understand protein–RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins.

Roles of conformational disorder and downhill folding in modulating protein-DNA recognition

Transcription factors are thought to efficiently search for their target DNA site via a combination of conventional 3D diffusion and 1D diffusion along the DNA molecule mediated by non-specific electrostatic interactions. This process requires the DNA-binding protein to quickly exchange between a search competent and a target recognition mode, but little is known as to how these two binding modes are encoded in the conformational properties of the protein. Here, we investigate this issue on the engrailed homeodomain (EngHD), a DNA-binding domain that folds ultrafast and exhibits a complex conformational behavior consistent with the downhill folding scenario. We explore the interplay between folding and DNA recognition using a coarse-grained computational model that allows us to manipulate the folding properties of the protein and monitor its non-specific and specific binding to DNA. We find that conformational disorder increases the search efficiency of EngHD by promoting a fast gliding search mode in addition to sliding. When gliding, EngHD remains loosely bound to DNA moving linearly along its length. A partially disordered EngHD also binds more dynamically to the target site, reducing the half-life of the specific complex via a spring-loaded mechanism. These findings apply to all conditions leading to partial disorder. However, we also find that at physiologically relevant temperatures EngHD is well folded and can only obtain the conformational flexibility required to accelerate 1D diffusion when it folds/unfolds within the downhill scenario (crossing a marginal free energy barrier). In addition, the conformational flexibility of native downhill EngHD enables its fast reconfiguration to lock into the specific binding site upon arrival, thereby affording finer control of the on- and off-rates of the specific complex. Our results provide key mechanistic insights into how DNA-binding domains optimize specific DNA recognition through the control of their conformational dynamics and folding mechanism.

Site-directed fluorescence labeling of intrinsically disordered region of human transcription factor YY1: The inhibitory effect of zinc ions

Site-specific labeling of proteins with fluorescent dyes allows the study of protein structure and function using a wide variety of fluorescent techniques. However, specific labeling is not trivial in the case of proteins containing multiple cysteine residues. An example of such a protein is transcription factor Yin Yang 1, which comprises eight cysteine residues in four C2H2 type zinc fingers in the C-terminal region. Kinetic measurements of the labeling process allowed us to develop preparative labeling of three cysteine residues differently introduced to the N-terminal, disordered fragment of the protein. The protocol developed in the present study allows to prepare the protein with high recovery yield and high selectivity of the labeling. This was confirmed using proteolytic digestion and spectroscopic approach. The labeling process was significantly affected by the presence of zinc ions and was dependent on the localization of the engineered cysteine residue. This is the first known example of the use of cysteine metal protection and labeling (CyMPL) technology for the labeling of protein regions with no stable secondary structures.

Conditionally disordered proteins: bringing the environment back into the fold

For many proteins, biological function requires the folding of the polypeptide chain into a unique and persistent tertiary structure. This review concerns proteins that adopt a specific tertiary structure to function, but are otherwise partially or completely disordered. The biological cue for protein folding is environmental perturbation or minor post-translational modification. Hence, we term these proteins conditionally disordered. Many of these proteins recognize and bind other molecules, and conditional disorder has been hypothesized to allow for more nuanced control and regulation of binding processes. However, this remains largely unproven. The sequences of conditionally disordered proteins suggest their propensity to fold; yet, under the standard laboratory conditions, they do not do so, which may appear surprising. We argue that the surprise results from the failure to consider the role of the environment in protein structure formation and that conditional disorder arises as a natural consequence of the marginal stability of the folded state.

When fast is better: protein folding fundamentals and mechanisms from ultrafast approaches

Protein folding research stalled for decades because conventional experiments indicated that proteins fold slowly and in single strokes, whereas theory predicted a complex interplay between dynamics and energetics resulting in myriad microscopic pathways. Ultrafast kinetic methods turned the field upside down by providing the means to probe fundamental aspects of folding, test theoretical predictions and benchmark simulations. Accordingly, experimentalists could measure the timescales for all relevant folding motions, determine the folding speed limit and confirm that folding barriers are entropic bottlenecks. Moreover, a catalogue of proteins that fold extremely fast (microseconds) could be identified. Such fast-folding proteins cross shallow free energy barriers or fold downhill, and thus unfold with minimal co-operativity (gradually). A new generation of thermodynamic methods has exploited this property to map folding landscapes, interaction networks and mechanisms at nearly atomic resolution. In parallel, modern molecular dynamics simulations have finally reached the timescales required to watch fast-folding proteins fold and unfold in silico. All of these findings have buttressed the fundamentals of protein folding predicted by theory, and are now offering the first glimpses at the underlying mechanisms. Fast folding appears to also have functional implications as recent results connect downhill folding with intrinsically disordered proteins, their complex binding modes and ability to moonlight. These connections suggest that the coupling between downhill (un)folding and binding enables such protein domains to operate analogically as conformational rheostats.

Toxoplasma Effectors Targeting Host Signaling and Transcription

Early electron microscopy studies revealed the elaborate cellular features that define the unique adaptations of apicomplexan parasites. Among these were bulbous rhoptry (ROP) organelles and small, dense granules (GRAs), both of which are secreted during invasion of host cells. These early morphological studies were followed by the exploration of the cellular contents of these secretory organelles, revealing them to be comprised of highly divergent protein families with few conserved domains or predicted functions. In parallel, studies on host-pathogen interactions identified many host signaling pathways that were mysteriously altered by infection. It was only with the advent of forward and reverse genetic strategies that the connections between individual parasite effectors and the specific host pathways that they targeted finally became clear. The current repertoire of parasite effectors includes ROP kinases and pseudokinases that are secreted during invasion and that block host immune pathways. Similarly, many secretory GRA proteins alter host gene expression by activating host transcription factors, through modification of chromatin, or by inducing small noncoding RNAs. These effectors highlight novel mechanisms by which T. gondii has learned to harness host signaling to favor intracellular survival and will guide future studies designed to uncover the additional complexity of this intricate host-pathogen interaction.

Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind

Intrinsically disordered proteins and regions (IDPs and IDRs) are involved in a wide range of cellular functions and they often facilitate interactions with RNAs, DNAs, and proteins. Although many computational methods can predict IDPs and IDRs in protein sequences, only a few methods predict their functions and these functions primarily concern protein binding. We describe how to use the first computational method DisoRDPbind for high-throughput prediction of multiple functions of disordered regions. Our method predicts the RNA-, DNA-, and protein-binding residues located in IDRs in the input protein sequences. DisoRDPbind provides accurate predictions and is sufficiently fast to make predictions for full genomes. Our method is implemented as a user-friendly webserver that is freely available at http://biomine.ece.ualberta.ca/DisoRDPbind/ . We overview our predictor, discuss how to run the webserver, and show how to interpret the corresponding results. We also demonstrate the utility of our method based on two case studies, human BRCA1 protein that binds various proteins and DNA, and yeast 60S ribosomal protein L4 that interacts with proteins and RNA.

A structural perspective of RNA recognition by intrinsically disordered proteins

Protein-RNA recognition is essential for gene expression and its regulation, which is indispensable for the survival of the living organism at one hand, on the other hand, misregulation of this recognition may lead to their extinction. Polymorphic conformation of both the interacting partners is a characteristic feature of such molecular recognition that promotes the assembly. Many RNA binding proteins (RBP) or regions in them are found to be intrinsically disordered, and this property helps them to play a central role in the regulatory processes. Sequence composition and the length of the flexible linkers between RNA binding domains in RBPs are crucial in making significant contacts with its partner RNA. Polymorphic conformations of RBPs can provide thermodynamic advantage to its binding partner while acting as a chaperone. Prolonged extensions of the disordered regions in RBPs also contribute to the stability of the large cellular machines including ribosome and viral assemblies. The involvement of these disordered regions in most of the significant cellular processes makes RBPs highly associated with various human diseases that arise due to their misregulation.

Protein intrinsic disorder within the Potyvirus genus: from proteome-wide analysis to functional annotation

Within proteins, intrinsically disordered regions (IDRs) are devoid of stable secondary and tertiary structures under physiological conditions and rather exist as dynamic ensembles of inter-converting conformers. Although ubiquitous in all domains of life, the intrinsic disorder content is highly variable in viral genomes. Over the years, functional annotations of disordered regions at the scale of the whole proteome have been conducted for several animal viruses. But to date, similar studies applied to plant viruses are still missing. Based on disorder prediction tools combined with annotation programs and evolutionary studies, we analyzed the intrinsic disorder content in Potyvirus, using a 10-species dataset representative of this genus diversity. In this paper, we revealed that: (i) the Potyvirus proteome displays high disorder content, (ii) disorder is conserved during Potyvirus evolution, suggesting a functional advantage of IDRs, (iii) IDRs evolve faster than ordered regions, and (iv) IDRs may be associated with major biological functions required for the Potyvirus cycle. Notably, the proteins P1, Coat protein (CP) and Viral genome-linked protein (VPg) display a high content of conserved disorder, enriched in specific motifs mimicking eukaryotic functional modules and suggesting strategies of host machinery hijacking. In these three proteins, IDRs are particularly conserved despite their high amino acid polymorphism, indicating a link to adaptive processes. Through this comprehensive study, we further investigate the biological relevance of intrinsic disorder in Potyvirus biology and we propose a functional annotation of potyviral proteome IDRs.

A structural perspective of RNA recognition by intrinsically disordered proteins

Protein-RNA recognition is essential for gene expression and its regulation, which is indispensable for the survival of the living organism at one hand, on the other hand, misregulation of this recognition may lead to their extinction. Polymorphic conformation of both the interacting partners is a characteristic feature of such molecular recognition that promotes the assembly. Many RNA binding proteins (RBP) or regions in them are found to be intrinsically disordered, and this property helps them to play a central role in the regulatory processes. Sequence composition and the length of the flexible linkers between RNA binding domains in RBPs are crucial in making significant contacts with its partner RNA. Polymorphic conformations of RBPs can provide thermodynamic advantage to its binding partner while acting as a chaperone. Prolonged extensions of the disordered regions in RBPs also contribute to the stability of the large cellular machines including ribosome and viral assemblies. The involvement of these disordered regions in most of the significant cellular processes makes RBPs highly associated with various human diseases that arise due to their misregulation.

Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.

The new (dis)order in RNA regulation

RNA-binding proteins play a key role in the regulation of all aspects of RNA metabolism, from the synthesis of RNA to its decay. Protein-RNA interactions have been thought to be mostly mediated by canonical RNA-binding domains that form stable secondary and tertiary structures. However, a number of pioneering studies over the past decades, together with recent proteome-wide data, have challenged this view, revealing surprising roles for intrinsically disordered protein regions in RNA binding. Here, we discuss how disordered protein regions can mediate protein-RNA interactions, conceptually grouping these regions into RS-rich, RG-rich, and other basic sequences, that can mediate both specific and non-specific interactions with RNA. Disordered regions can also influence RNA metabolism through protein aggregation and hydrogel formation. Importantly, protein-RNA interactions mediated by disordered regions can influence nearly all aspects of co- and post-transcriptional RNA processes and, consequently, their disruption can cause disease. Despite growing interest in disordered protein regions and their roles in RNA biology, their mechanisms of binding, regulation, and physiological consequences remain poorly understood. In the coming years, the study of these unorthodox interactions will yield important insights into RNA regulation in cellular homeostasis and disease.

A transducible nuclear/nucleolar protein, mLLP, regulates neuronal morphogenesis and synaptic transmission

Cell-permeable proteins are emerging as unconventional regulators of signal transduction and providing a potential for therapeutic applications. However, only a few of them are identified and studied in detail. We identify a novel cell-permeable protein, mouse LLP homolog (mLLP), and uncover its roles in regulating neural development. We found that mLLP is strongly expressed in developing nervous system and that mLLP knockdown or overexpression during maturation of cultured neurons affected the neuronal growth and synaptic transmission. Interestingly, extracellular addition of mLLP protein enhanced dendritic arborization, demonstrating the non-cell-autonomous effect of mLLP. Moreover, mLLP interacts with CCCTC-binding factor (CTCF) as well as transcriptional machineries and modulates gene expression involved in neuronal growth. Together, these results illustrate the characteristics and roles of previously unknown cell-permeable protein mLLP in modulating neural development.