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

Recent years witnessed increased interest in intrinsically disordered proteins and regions. These proteins and regions are abundant and possess unique structural features and a broad functional repertoire that complements ordered proteins. However, modern studies on the abundance and functions of intrinsically disordered proteins and regions are relatively limited in size and scope of their analysis. To fill this gap, we performed a broad and detailed computational analysis of over 6 million proteins from 59 archaea, 471 bacterial, 110 eukaryotic and 325 viral proteomes. We used arguably more accurate consensus-based disorder predictions, and for the first time comprehensively characterized intrinsic disorder at proteomic and protein levels from all significant perspectives, including abundance, cellular localization, functional roles, evolution, and impact on structural coverage. We show that intrinsic disorder is more abundant and has a unique profile in eukaryotes. We map disorder into archaea, bacterial and eukaryotic cells, and demonstrate that it is preferentially located in some cellular compartments. Functional analysis that considers over 1,200 annotations shows that certain functions are exclusively implemented by intrinsically disordered proteins and regions, and that some of them are specific to certain domains of life. We reveal that disordered regions are often targets for various post-translational modifications, but primarily in the eukaryotes and viruses. Using a phylogenetic tree for 14 eukaryotic and 112 bacterial species, we analyzed relations between disorder, sequence conservation and evolutionary speed. We provide a complete analysis that clearly shows that intrinsic disorder is exceptionally and uniquely abundant in each domain of life.

Covering complete proteomes with X-ray structures: a current snapshot.

Structural genomics programs have developed and applied structure-determination pipelines to a wide range of protein targets, facilitating the visualization of macromolecular interactions and the understanding of their molecular and biochemical functions. The fundamental question of whether three-dimensional structures of all proteins and all functional annotations can be determined using X-ray crystallography is investigated. A first-of-its-kind large-scale analysis of crystallization propensity for all proteins encoded in 1953 fully sequenced genomes was performed. It is shown that current X-ray crystallographic knowhow combined with homology modeling can provide structures for 25% of modeling families (protein clusters for which structural models can be obtained through homology modeling), with at least one structural model produced for each Gene Ontology functional annotation. The coverage varies between superkingdoms, with 19% for eukaryotes, 35% for bacteria and 49% for archaea, and with those of viruses following the coverage values of their hosts. It is shown that the crystallization propensities of proteomes from the taxonomic superkingdoms are distinct. The use of knowledge-based target selection is shown to substantially increase the ability to produce X-ray structures. It is demonstrated that the human proteome has one of the highest attainable coverage values among eukaryotes, and GPCR membrane proteins suitable for X-ray structure determination were determined.

Human structural proteome-wide characterization of Cyclosporine A targets.

MOTIVATION: Off-target interactions of a popular immunosuppressant Cyclosporine A (CSA) with several proteins besides its molecular target, cyclophilin A, are implicated in the activation of signaling pathways that lead to numerous side effects of this drug. RESULTS: Using structural human proteome and a novel algorithm for inverse ligand binding prediction, ILbind, we determined a comprehensive set of 100+ putative partners of CSA. We empirically show that predictive quality of ILbind is better compared with other available predictors for this compound. We linked the putative target proteins, which include many new partners of CSA, with cellular functions, canonical pathways and toxicities that are typical for patients who take this drug. We used complementary approaches (molecular docking, molecular dynamics, surface plasmon resonance binding analysis and enzymatic assays) to validate and characterize three novel CSA targets: calpain 2, caspase 3 and p38 MAP kinase 14. The three targets are involved in the apoptotic pathways, are interconnected and are implicated in nephrotoxicity.

Interplay between the oxidoreductase PDIA6 and microRNA-322 controls the response to disrupted endoplasmic reticulum calcium homeostasis.

The disruption of the energy or nutrient balance triggers endoplasmic reticulum (ER) stress, a process that mobilizes various strategies, collectively called the unfolded protein response (UPR), which reestablish homeostasis of the ER and cell. Activation of the UPR stress sensor IRE1α (inositol-requiring enzyme 1α) stimulates its endoribonuclease activity, leading to the generation of the mRNA encoding the transcription factor XBP1 (X-box binding protein 1), which regulates the transcription of genes encoding factors involved in controlling the quality and folding of proteins. We found that the activity of IRE1α was regulated by the ER oxidoreductase PDIA6 (protein disulfide isomerase A6) and the microRNA miR-322 in response to disruption of ER Ca2+ homeostasis. PDIA6 interacted with IRE1α and enhanced IRE1α activity as monitored by phosphorylation of IRE1α and XBP1 mRNA splicing, but PDIA6 did not substantially affect the activity of other pathways that mediate responses to ER stress. ER Ca2+ depletion and activation of store-operated Ca2+ entry reduced the abundance of the microRNA miR-322, which increased PDIA6 mRNA stability and, consequently, IRE1α activity during the ER stress response. In vivo experiments with mice and worms showed that the induction of ER stress correlated with decreased miR-322 abundance, increased PDIA6 mRNA abundance, or both. Together, these findings demonstrated that ER Ca2+, PDIA6, IRE1α, and miR-322 function in a dynamic feedback loop modulating the UPR under conditions of disrupted ER Ca2+ homeostasis.

Prediction of intrinsic disorder in proteins using MFDp2.

Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant across all kingdoms of life, particularly in eukaryotes, and are implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel webserver for accurate sequence-based prediction of protein disorder which also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility, and alignments to chains with annotated disorder. The webserver allows predictions for multiple proteins at the same time, includes help pages and tutorial, and the results can be downloaded as text-based (parsable) file. MFDp2 is freely available at http://biomine.ece.ualberta.ca/MFDp2/.

A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome.

Intrinsic disorder (i.e., lack of a unique 3-D structure) is a common phenomenon, and many biologically active proteins are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions constitute a significant part of all proteomes, and their functional repertoire is complementary to functions of ordered proteins. In fact, intrinsic disorder represents an important driving force for many specific functions. An illustrative example of such disorder-centric functional class is RNA-binding proteins. In this study, we present the results of comprehensive bioinformatics analyses of the abundance and roles of intrinsic disorder in 3,411 ribosomal proteins from 32 species. We show that many ribosomal proteins are intrinsically disordered or hybrid proteins that contain ordered and disordered domains. Predicted globular domains of many ribosomal proteins contain noticeable regions of intrinsic disorder. We also show that disorder in ribosomal proteins has different characteristics compared to other proteins that interact with RNA and DNA including overall abundance, evolutionary conservation, and involvement in protein-protein interactions. Furthermore, intrinsic disorder is not only abundant in the ribosomal proteins, but we demonstrate that it is absolutely necessary for their various functions.

Genome-scale prediction of proteins with long intrinsically disordered regions.

Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/.

Prediction and characterization of cyclic proteins from sequences in three domains of life.

Cyclic proteins (CPs) have circular chains with a continuous cycle of peptide bonds. Their unique structural traits result in greater stability and resistance to degradation when compared to their acyclic counterparts. They are also promising targets for pharmaceutical/therapeutic applications. To date, only a few hundred CPs are known, although recent studies suggest that their numbers might be substantially higher. Here we developed a first-of-its-kind, accurate and high-throughput method called CyPred that predicts whether a given protein chain is cyclic. CyPred considers currently well-represented CP families: cyclotides, cyclic defensins, bacteriocins, and trypsin inhibitors. Empirical tests demonstrate that CyPred outperforms commonly used alignment methods. We used CyPred to estimate the incidence of CPs and found ~3500 putative CPs among 5.7+ million chains from 642 fully sequenced proteomes from archaea, bacteria, and eukaryotes. The median number of putative CPs per species ranges from three for archaea proteomes to two for eukaryotes/bacteria, with 7% of archaea, 11% of bacterial, and 16% of eukaryotic proteomes having 10+ CPs. The differences in the estimated fractions of CPs per proteome are as large as three orders of magnitude. Among eukaryotes, animals have higher ratios of CPs compared to fungi, while plants have the largest spread of the ratios. We also show that proteomes enriched in cyclic proteins evolve more slowly than proteomes with fewer cyclic chains. Our results suggest that further research is needed to fully uncover the scope and potential of cyclic proteins. A list of putative CPs and the CyPred method are available at http://biomine.ece.ualberta.ca/CyPred/. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

RAPID: fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale.

Recent research in the protein intrinsic disorder was stimulated by the availability of accurate computational predictors. However, most of these methods are relatively slow, especially considering proteome-scale applications, and were shown to produce relatively large errors when estimating disorder at the protein- (in contrast to residue-) level, which is defined by the fraction/content of disordered residues. To this end, we propose a novel support vector Regression-based Accurate Predictor of Intrinsic Disorder (RAPID). Key advantages of RAPID are speed (prediction of an average-size eukaryotic proteome takes <1h on a modern desktop computer); sophisticated design (multiple, complementary information sources that are aggregated over an input chain are combined using feature selection); and high-quality and robust predictive performance. Empirical tests on two diverse benchmark datasets reveal that RAPID's predictive performance compares favorably to a comprehensive set of state-of-the-art disorder and disorder content predictors. Drawing on high speed and good predictive quality, RAPID was used to perform large-scale characterization of disorder in 200+ fully sequenced eukaryotic proteomes. Our analysis reveals interesting relations of disorder with structural coverage and chain length, and unusual distribution of fully disordered chains. We also performed a comprehensive (using 56000+ annotated chains, which doubles the scope of previous studies) investigation of cellular functions and localizations that are enriched in the disorder in the human proteome. RAPID, which allows for batch (proteome-wide) predictions, is available as a web server at http://biomine.ece.ualberta.ca/RAPID/.

Stochastic machines as a colocalization mechanism for scaffold protein function.

The axis inhibition (Axin) scaffold protein colocalizes ß-catenin, casein kinase Iα, and glycogen synthetase kinase 3ß by their binding to Axin's long intrinsically disordered region, thereby yielding structured domains with flexible linkers. This complex leads to the phosphorylation of ß-catenin, marking it for destruction. Fusing proteins with flexible linkers vastly accelerates chemical interactions between them by their colocalization. Here we propose that the complex works by random movements of a "stochastic machine," not by coordinated conformational changes. This non-covalent, modular assembly process allows the various molecular machine components to be used in multiple processes.

Monocytes from patients with osteoarthritis display increased osteoclastogenesis and bone resorption: the In Vitro Osteoclast Differentiation in Arthritis study.

OBJECTIVE: To compare the osteoclastogenic capacity of peripheral blood mononuclear cells (PBMCs) from patients with osteoarthritis (OA) to that of PBMCs from self-reported normal individuals. METHODS: PBMCs from 140 patients with OA and 45 healthy donors were assayed for CD14+ expression and induced to differentiate into osteoclasts over 3 weeks in vitro. We assessed the number of osteoclasts, their resorptive activity, osteoclast apoptosis, and expression of the following cytokine receptors: RANK, interleukin-1 receptor type I (IL-1RI), and IL-1RII. A ridge logistic regression classifier was developed to discriminate OA patients from controls. RESULTS: PBMCs from OA patients gave rise to more osteoclasts that resorbed more bone surface than did PBMCs from controls. The number of CD14+ precursors was comparable in both groups, but there was less apoptosis in osteoclasts obtained from OA patients. Although no correlation was found between osteoclastogenic capacity and clinical or radiographic scores, levels of IL-1RI were significantly lower in cultures from patients with OA than in cultures from controls. Osteoclast apoptosis and expression levels of IL-1RI and IL-1RII were used to build a multivariate predictive model for OA. CONCLUSION: During 3 weeks of culture under identical conditions, monocytes from patients with OA display enhanced capacity to generate osteoclasts compared to cells from controls. Enhanced osteoclastogenesis is accompanied by increased resorptive activity, reduced osteoclast apoptosis, and diminished IL-1RI expression. These findings support the possibility that generalized changes in bone metabolism affecting osteoclasts participate in the pathophysiology of OA.

D²P²: database of disordered protein predictions.

We present the Database of Disordered Protein Prediction (D(2)P(2)), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D(2)P(2) will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.

MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles.

Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/.

Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions.

Inverse ligand binding prediction utilizes a few protein-ligand (drug) complexes to predict other secondary therapeutic and off-targets of a given drug molecule on a proteomic scale. We adapt two binding site predictors, FINDSITE and SMAP, to perform the inverse predictions and evaluate them on over 30 representative ligands. Use of just one complex allows the identification of other protein targets; the availability of additional complexes improves the results. Both methods offer comparable quality when using three complexes with diverse proteins. SMAP is better when fewer complexes are available, while FINDSITE provides stronger predictions for smaller ligands. We propose a consensus that combines (and outperforms) the two complementary approaches implemented by FINDSITE and SMAP. Most importantly, we demonstrate that these methods successfully find distant targets that belong to structurally different folds compared to the proteins in the input complexes.

MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins.

MOTIVATION: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. RESULTS: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, ß, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues. AVAILABILITY: http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf.

More than just tails: intrinsic disorder in histone proteins.

Many biologically active proteins are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions are very common in nature, abundantly found in all organisms, where they carry out important biological functions. The functions of these proteins complement the functional repertoire of "normal" ordered proteins, and many protein functional classes are heavily dependent on intrinsic disorder. Among these disorder-centric functions are interactions with nucleic acids and protein complex assembly. In this study, we present the results of comprehensive bioinformatics analyses of the abundance and roles of intrinsic disorder in 2007 histones from 746 species. We show that all the members of the histone family are intrinsically disordered proteins. Furthermore, intrinsic disorder is not only abundant in histones, but is absolutely necessary for various histone functions, starting from heterodimerization to formation of higher order oligomers, to interactions with DNA and other proteins, and to posttranslational modifications.

CRYSpred: accurate sequence-based protein crystallization propensity prediction using sequence-derived structural characteristics.

Relatively low success rates of X-ray crystallography, which is the most popular method for solving proteins structures, motivate development of novel methods that support selection of tractable protein targets. This aspect is particularly important in the context of the current structural genomics efforts that allow for a certain degree of flexibility in the target selection. We propose CRYSpred, a novel in-silico crystallization propensity predictor that uses a set of 15 novel features which utilize a broad range of inputs including charge, hydrophobicity, and amino acid composition derived from the protein chain, and the solvent accessibility and disorder predicted from the protein sequence. Our method outperforms seven modern crystallization propensity predictors on three, independent from training dataset, benchmark test datasets. The strong predictive performance offered by the CRYSpred is attributed to the careful design of the features, utilization of the comprehensive set of inputs, and the usage of the Support Vector Machine classifier. The inputs utilized by CRYSpred are well-aligned with the existing rules-of-thumb that are used in the structural genomics studies.

Protein intrinsic disorder as a flexible armor and a weapon of HIV-1.

Many proteins and protein regions are disordered in their native, biologically active states. These proteins/regions are abundant in different organisms and carry out important biological functions that complement the functional repertoire of ordered proteins. Viruses, with their highly compact genomes, small proteomes, and high adaptability for fast change in their biological and physical environment utilize many of the advantages of intrinsic disorder. In fact, viral proteins are generally rich in intrinsic disorder, and intrinsically disordered regions are commonly used by viruses to invade the host organisms, to hijack various host systems, and to help viruses in accommodation to their hostile habitats and to manage their economic usage of genetic material. In this review, we focus on the structural peculiarities of HIV-1 proteins, on the abundance of intrinsic disorder in viral proteins, and on the role of intrinsic disorder in their functions.

Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors.

MOTIVATION: Nucleotides are multifunctional molecules that are essential for numerous biological processes. They serve as sources for chemical energy, participate in the cellular signaling and they are involved in the enzymatic reactions. The knowledge of the nucleotide-protein interactions helps with annotation of protein functions and finds applications in drug design. RESULTS: We propose a novel ensemble of accurate high-throughput predictors of binding residues from the protein sequence for ATP, ADP, AMP, GTP and GDP. Empirical tests show that our NsitePred method significantly outperforms existing predictors and approaches based on sequence alignment and residue conservation scoring. The NsitePred accurately finds more binding residues and binding sites and it performs particularly well for the sites with residues that are clustered close together in the sequence. The high predictive quality stems from the usage of novel, comprehensive and custom-designed inputs that utilize information extracted from the sequence, evolutionary profiles, several sequence-predicted structural descriptors and sequence alignment. Analysis of the predictive model reveals several sequence-derived hallmarks of nucleotide-binding residues; they are usually conserved and flanked by less conserved residues, and they are associated with certain arrangements of secondary structures and amino acid pairs in the specific neighboring positions in the sequence. AVAILABILITY: http://biomine.ece.ualberta.ca/nSITEpred/ CONTACT: lkurgan@ece.ualberta.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ATPsite: sequence-based prediction of ATP-binding residues.

BACKGROUND: ATP is a ubiquitous nucleotide that provides energy for cellular activities, catalyzes chemical reactions, and is involved in cellular signalling. The knowledge of the ATP-protein interactions helps with annotation of protein functions and finds applications in drug design. The sequence to structure annotation gap motivates development of high-throughput sequence-based predictors of the ATP-binding residues. Moreover, our empirical tests show that the only existing predictor, ATPint, is characterized by relatively low predictive quality. METHODS: We propose a novel, high-throughput machine learning-based predictor, ATPsite, which identifies ATP-binding residues from protein sequences. Our predictor utilizes Support Vector Machine classifier and a comprehensive set of input features that are based on the sequence, evolutionary profiles, and the sequence-predicted structural descriptors including secondary structure, solvent accessibility, and dihedral angles. RESULTS: The ATPsite achieves significantly higher Mathews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values when compared with the existing methods including the ATPint, conservation-based rate4site, and alignment-based BLAST predictors. We also assessed the effectiveness of individual input types. The PSSM profile, the conservation scores, and certain features based on amino acid groups are shown to be more effective in predicting the ATP-binding residues than the remaining feature groups. CONCLUSIONS: Statistical tests show that ATPsite significantly outperforms existing solutions. The consensus of the ATPsite with the sequence-alignment based predictor is shown to give further improvements.