Marcin J. Mizianty

D2P2: database of disordered protein predictions

We present the Database of Disordered Protein Prediction (D2P2), 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. D2P2 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.

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, β, 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 Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources

Motivation: Intrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed. Results: We propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with ≤25% similarity to the test sequences, our method consistently and significantly outperforms the other methods based on the MCC index. The MFDp outperforms modern disorder predictors for the binary disorder assignment and provides competitive real-valued predictions. The MFDps outputs are also shown to outperform the other methods in the identification of proteins with long disordered regions. Availability: http://biomine.ece.ualberta.ca/MFDp.html Supplementary information: Supplementary data are available at Bioinformatics online. Contact: lkurgan@ece.ualberta.ca

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.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

BackgroundKnowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.ResultsThe proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.ConclusionsThe improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

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.

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.

Interplay Between the Oxidoreductase PDIA6 and microRNA-322 Controls the Response to Disrupted Endoplasmic Reticulum Calcium Homeostasis

Depletion of Ca2+ in the endoplasmic reticulum favors activation of a stress response involving IRE1α. Responding the Right Way to Cellular Stress Some proteins must be folded correctly in the endoplasmic reticulum (ER) to function properly. Various stress conditions can cause the buildup of unfolded proteins in the ER, which can cause cell death. There are multiple ways in which cells can respond to deal with the buildup of unfolded proteins. Groenendyk et al. investigated how cells deal with the stress of depletion of calcium ions from the ER and identified a pathway involving a microRNA and an oxidoreductase in the ER. They found that depletion of calcium from the ER resulted in the decreased abundance of a microRNA, which enabled a target mRNA and the oxidoreductase it encoded to accumulate. The oxidoreductase then activated a specific stress response. The authors showed that this pathway could be present in mice and nematodes. 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.

Sequence-based prediction of protein crystallization, purification and production propensity

Motivation: X-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions. Results: The proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments. Availability: http://biomine.ece.ualberta.ca/PPCpred/. Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online.