MOBI: a web server to define and visualize structural mobility in NMR protein ensembles

Liaisons dangereuses: Intrinsic Disorder in Cellular Proteins Recruited to Viral Infection-Related Biocondensates

Liquid-liquid phase separation (LLPS) is responsible for the formation of so-called membrane-less organelles (MLOs) that are essential for the spatio-temporal organization of the cell. Intrinsically disordered proteins (IDPs) or regions (IDRs), either alone or in conjunction with nucleic acids, are involved in the formation of these intracellular condensates. Notably, viruses exploit LLPS at their own benefit to form viral replication compartments. Beyond giving rise to biomolecular condensates, viral proteins are also known to partition into cellular MLOs, thus raising the question as to whether these cellular phase-separating proteins are drivers of LLPS or behave as clients/regulators. Here, we focus on a set of eukaryotic proteins that are either sequestered in viral factories or colocalize with viral proteins within cellular MLOs, with the primary goal of gathering organized, predicted, and experimental information on these proteins, which constitute promising targets for innovative antiviral strategies. Using various computational approaches, we thoroughly investigated their disorder content and inherent propensity to undergo LLPS, along with their biological functions and interactivity networks. Results show that these proteins are on average, though to varying degrees, enriched in disorder, with their propensity for phase separation being correlated, as expected, with their disorder content. A trend, which awaits further validation, tends to emerge whereby the most disordered proteins serve as drivers, while more ordered cellular proteins tend instead to be clients of viral factories. In light of their high disorder content and their annotated LLPS behavior, most proteins in our data set are drivers or co-drivers of molecular condensation, foreshadowing a key role of these cellular proteins in the scaffolding of viral infection-related MLOs.

Crystal structure of the phospholipase A and acyltransferase 4 (PLAAT4) catalytic domain

Phospholipase A and Acyltransferase 4 (PLAAT4) is a class II tumor suppressor, that also plays a role as a restrictor of intracellular Toxoplasma gondii infection through restriction of parasitic vacuole size. The catalytic N-terminal domain (NTD) interacts with the C-terminal domain (CTD), which is important for sub-cellular targeting and enzymatic function. The dynamics of the NTD main (L1) loop and the L2(B6) loop adjacent to the active site, have been shown to be important regulators of enzymatic activity. Here, we present the crystal structure of PLAAT4 NTD, determined from severely intergrown crystals using automated, laser-based crystal harvesting and data reduction technologies. The structure showed the L1 loop in two distinct conformations, highlighting a complex network of interactions likely influencing its conformational flexibility. Ensemble refinement of the crystal structure recapitulates the major correlated motions observed in solution by NMR. Our analysis offers useful insights on millisecond dynamics based on the crystal structure, complementing NMR studies which preclude structural information at this time scale.

MobiDB: 10 years of intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

ODiNPred: comprehensive prediction of protein order and disorder

Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

Exploring Protein Intrinsic Disorder with MobiDB

Nowadays, it is well established that many proteins or regions under physiological conditions lack a fixed three-dimensional structure and are intrinsically disordered. MobiDB is the main repository of protein disorder and mobility annotations, combining different data sources to provide an exhaustive overview of intrinsic disorder. MobiDB includes curated annotations from other databases, indirect disorder evidence from structural data, and disorder predictions from protein sequences. It provides an easy-to-use web server to visualize and explore disorder information. This chapter describes the data available in MobiDB, emphasizing how to use and access the intrinsic disorder data. MobiDB is available at URL http://mobidb.bio.unipd.it .

Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

Forkhead Domains of FOXO Transcription Factors Differ in both Overall Conformation and Dynamics

FOXO transcription factors regulate cellular homeostasis, longevity and response to stress. FOXO1 (also known as FKHR) is a key regulator of hepatic glucose production and lipid metabolism, and its specific inhibition may have beneficial effects on diabetic hyperglycemia by reducing hepatic glucose production. Moreover, all FOXO proteins are considered potential drug targets for drug resistance prevention in cancer therapy. However, the development of specific FOXO inhibitors requires a detailed understanding of structural differences between individual FOXO DNA-binding domains. The high-resolution structure of the DNA-binding domain of FOXO1 reported in this study and its comparison with structures of other FOXO proteins revealed differences in both their conformation and flexibility. These differences are encoded by variations in protein sequences and account for the distinct functions of FOXO proteins. In particular, the positions of the helices H1, H2 and H3, whose interface form the hydrophobic core of the Forkhead domain, and the interactions between hydrophobic residues located on the interface between the N-terminal segment, the H2-H3 loop, and the recognition helix H3 differ among apo FOXO1, FOXO3 and FOXO4 proteins. Therefore, the availability of apo structures of DNA-binding domains of all three major FOXO proteins will support the development of FOXO-type-specific inhibitors.

MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins

The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility.

On the Need to Develop Guidelines for Characterizing and Reporting Intrinsic Disorder in Proteins

Since the early 2000s, numerous computational tools have been created and used to predict intrinsic disorder in proteins. At present, the output from these algorithms is difficult to interpret in the absence of standards or references for comparison. There are many reasons to establish a set of standard-based guidelines to evaluate computational protein disorder predictions. This viewpoint explores a handful of these reasons, including standardizing nomenclature to improve communication, rigor and reproducibility, and making it easier for newcomers to enter the field. An approach for reporting predicted disorder in single proteins with respect to whole proteomes is discussed. The suggestions are not intended to be formulaic; they should be viewed as a starting point to establish guidelines for interpreting and reporting computational protein disorder predictions.

Interplay between sequence, structure and linear motifs in the adenovirus E1A hub protein

E1A is the main transforming protein in mastadenoviruses. This work uses bioinformatics to extrapolate experimental knowledge from Human adenovirus serotype 5 and 12 E1A proteins to all known serotypes. A conserved domain architecture with a high degree of intrinsic disorder acts as a scaffold for multiple linear motifs with variable occurrence mediating the interaction with over fifty host proteins. While linear motifs contribute strongly to sequence conservation within intrinsically disordered E1A regions, motif repertoires can deviate significantly from those found in prototypical serotypes. Close to one hundred predicted residue-residue contacts suggest the presence of stable structure in the CR3 domain and of specific conformational ensembles involving both short- and long-range intramolecular interactions. Our computational results suggest that E1A sequence conservation and co-evolution reflect the evolutionary pressure to maintain a mainly disordered, yet non-random conformation harboring a high number of binding motifs that mediate viral hijacking of the cell machinery.

Coil conversion to β-strand induced by dimerization

Most molecular processes in living organisms rely on protein-protein interactions, many of which are mediated by β-sheet interfaces; this study investigates the formation of β-sheet interfaces through the conversion of coils into β-strands. Following an exhaustive search in the Protein Data Bank, the corresponding structural dimorphic fragments were extracted, characterized, and analyzed. Their short strand lengths and specific amino acid profiles indicate that dimorphic β-strand interfaces are likely to be less stable than standard ones and could even convert to coil interfaces if their environment changes. Moreover, the construction of a simple classifier able to discriminate between the sequences of dimorphic and standard β-strand interfaces suggests that the nature of those dimorphic sequences could be predicted, providing a novel means of identifying proteins capable of forming dimers.

Structural dynamics of protein S1 on the 70S ribosome visualized by ensemble cryo-EM

Bacterial ribosomal protein S1 is the largest and highly flexible protein of the 30S subunit, and one of a few core ribosomal proteins for which a complete structure is lacking. S1 is thought to participate in transcription and translation. Best understood is the role of S1 in facilitating translation of mRNAs with structured 5' UTRs. Here, we present cryo-EM analyses of the 70S ribosome that reveal multiple conformations of S1. Based on comparison of several 3D maximum likelihood classification approaches in Frealign, we propose a streamlined strategy for visualizing a highly dynamic component of a large macromolecular assembly that itself exhibits high compositional and conformational heterogeneity. The resulting maps show how S1 docks at the ribosomal protein S2 near the mRNA exit channel. The globular OB-fold domains sample a wide area around the mRNA exit channel and interact with mobile tails of proteins S6 and S18. S1 also interacts with the mRNA entrance channel, where an OB-fold domain can be localized near S3 and S5. Our analyses suggest that S1 cooperates with other ribosomal proteins to form a dynamic mesh near the mRNA exit and entrance channels to modulate the binding, folding and movement of mRNA.

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein-protein, protein-RNA, and protein-DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.

Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions

Intrinsically disordaered proteins (IDPs) are a prevalent phenomenon with over 30% of human proteins estimated to have long disordered regions. Computational methods are widely used to study IDPs, however, nearly all treat disorder in a binary fashion, not accounting for the structural heterogeneity present in disordered regions. Here, we present a new de novo method, FRAGFOLD-IDP, which addresses this problem. Using 200 protein structural ensembles derived from NMR, we show that FRAGFOLD-IDP achieves superior results compared to methods which can predict related data (NMR order parameter, or crystallographic B-factor). FRAGFOLD-IDP produces very good predictions for 33.5% of cases and helps to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably predict per-residue fluctuations. It implies that disorder is a local property and it does not depend on the fold. Our results are orthogonal to DynaMine, the only other method significantly better than the naïve prediction. We therefore combine these two using a neural network. FRAGFOLD-IDP enables better insight into backbone dynamics in IDPs and opens exciting possibilities for the design of disordered ensembles, disorder-to-order transitions, or design for protein dynamics.

Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe

Intrinsic disorder (ID) in proteins has been extensively described for the last decade; a large-scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database derived from sequence-based predictions in MobiDB. Almost half the sequences contain an ID region of at least five residues. About 9% of proteins have a long ID region of over 20 residues which are more abundant in Eukaryotic organisms and most frequently cover less than 20% of the sequence. A small subset of about 67,000 (out of over 80 million) proteins is fully disordered and mostly found in Viruses. Most proteins have only one ID, with short ID evenly distributed along the sequence and long ID overrepresented in the center. The charged residue composition of Das and Pappu was used to classify ID proteins by structural propensities and corresponding functional enrichment. Swollen Coils seem to be used mainly as structural components and in biosynthesis in both Prokaryotes and Eukaryotes. In Bacteria, they are confined in the nucleoid and in Viruses provide DNA binding function. Coils & Hairpins seem to be specialized in ribosome binding and methylation activities. Globules & Tadpoles bind antigens in Eukaryotes but are involved in killing other organisms and cytolysis in Bacteria. The Undefined class is used by Bacteria to bind toxic substances and mediate transport and movement between and within organisms in Viruses. Fully disordered proteins behave similarly, but are enriched for glycine residues and extracellular structures.

A collection of intrinsic disorder characterizations from eukaryotic proteomes

Intrinsically disordered proteins and protein regions lack a stable three-dimensional structure under physiological conditions. Several proteomic investigations of intrinsic disorder have been performed to date and have found disorder to be prevalent in eukaryotic proteomes. Here we present descriptive statistics of intrinsic disorder features for ten model eukaryotic proteomes that have been calculated from computational disorder prediction algorithms. The data descriptor also provides consensus disorder annotations as well as additional physical parameters relevant to protein disorder, and further provides protein existence information for all proteins included in our analysis. The complete datasets can be downloaded freely, and it is envisaged that they will be updated periodically with new proteomes and protein disorder prediction algorithms. These datasets will be especially useful for assessing protein disorder, and conducting novel analyses that advance our understanding of intrinsic disorder and protein structure.

Structural dynamics of a methionine γ-lyase for calicheamicin biosynthesis: Rotation of the conserved tyrosine stacking with pyridoxal phosphate

CalE6 from Micromonospora echinospora is a (pyridoxal 5' phosphate) PLP-dependent methionine γ-lyase involved in the biosynthesis of calicheamicins. We report the crystal structure of a CalE6 2-(N-morpholino)ethanesulfonic acid complex showing ligand-induced rotation of Tyr100, which stacks with PLP, resembling the corresponding tyrosine rotation of true catalytic intermediates of CalE6 homologs. Elastic network modeling and crystallographic ensemble refinement reveal mobility of the N-terminal loop, which involves both tetrameric assembly and PLP binding. Modeling and comparative structural analysis of PLP-dependent enzymes involved in Cys/Met metabolism shine light on the functional implications of the intrinsic dynamic properties of CalE6 in catalysis and holoenzyme maturation.

Analysis of the interface variability in NMR structure ensembles of protein-protein complexes

NMR structures consist in ensembles of conformers, all satisfying the experimental restraints, which exhibit a certain degree of structural variability. We analyzed here the interface in NMR ensembles of protein-protein heterodimeric complexes and found it to span a wide range of different conservations. The different exhibited conservations do not simply correlate with the size of the systems/interfaces, and are most probably the result of an interplay between different factors, including the quality of experimental data and the intrinsic complex flexibility. In any case, this information is not to be missed when NMR structures of protein-protein complexes are analyzed; especially considering that, as we also show here, the first NMR conformer is usually not the one which best reflects the overall interface. To quantify the interface conservation and to analyze it, we used an approach originally conceived for the analysis and ranking of ensembles of docking models, which has now been extended to directly deal with NMR ensembles. We propose this approach, based on the conservation of the inter-residue contacts at the interface, both for the analysis of the interface in whole ensembles of NMR complexes and for the possible selection of a single conformer as the best representative of the overall interface. In order to make the analyses automatic and fast, we made the protocol available as a web tool at: https://www.molnac.unisa.it/BioTools/consrank/consrank-nmr.html.

MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins

MobiDB (http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein–protein interactions from STRING are also classified for disorder content.

Comprehensive large-scale assessment of intrinsic protein disorder

MOTIVATION

Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures.

RESULTS

MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10% are observed.

AVAILABILITY

The datasets are available from Web site at URL: http://mobidb.bio.unipd.it/lsd.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Visualising intrinsic disorder and conformational variation in protein ensembles

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination-however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human-computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

Intrinsically disordered proteins and intrinsically disordered protein regions

Intrinsically disordered proteins (IDPs) and IDP regions fail to form a stable structure, yet they exhibit biological activities. Their mobile flexibility and structural instability are encoded by their amino acid sequences. They recognize proteins, nucleic acids, and other types of partners; they accelerate interactions and chemical reactions between bound partners; and they help accommodate posttranslational modifications, alternative splicing, protein fusions, and insertions or deletions. Overall, IDP-associated biological activities complement those of structured proteins. Recently, there has been an explosion of studies on IDP regions and their functions, yet the discovery and investigation of these proteins have a long, mostly ignored history. Along with recent discoveries, we present several early examples and the mechanisms by which IDPs contribute to function, which we hope will encourage comprehensive discussion of IDPs and IDP regions in biochemistry textbooks. Finally, we propose future directions for IDP research.

Analysis and consensus of currently available intrinsic protein disorder annotation sources in the MobiDB database

BACKGROUND

Intrinsic protein disorder is becoming an increasingly important topic in protein science. During the last few years, intrinsically disordered proteins (IDPs) have been shown to play a role in many important biological processes, e.g. protein signalling and regulation. This has sparked a need to better understand and characterize different types of IDPs, their functions and roles. Our recently published database, MobiDB, provides a centralized resource for accessing and analysing intrinsic protein disorder annotations.

RESULTS

Here, we present a thorough description and analysis of the data made available by MobiDB, providing descriptive statistics on the various available annotation sources. Version 1.2.1 of the database contains annotations for ca. 4,500,000 UniProt sequences, covering all eukaryotic proteomes. In addition, we describe a novel consensus annotation calculation and its related weighting scheme. The comparison between disorder information sources highlights how the MobiDB consensus captures the main features of intrinsic disorder and correlates well with manually curated datasets. Finally, we demonstrate the annotation of 13 eukaryotic model organisms through MobiDB's datasets, and of an example protein through the interactive user interface.

CONCLUSIONS

MobiDB is a central resource for intrinsic disorder research, containing both experimental data and predictions. In the future it will be expanded to include additional information for all known proteins.

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.

MobiDB: a comprehensive database of intrinsic protein disorder annotations

MOTIVATION

Disordered protein regions are key to the function of numerous processes within an organism and to the determination of a protein's biological role. The most common source for protein disorder annotations, DisProt, covers only a fraction of the available sequences. Alternatively, the Protein Data Bank (PDB) has been mined for missing residues in X-ray crystallographic structures. Herein, we provide a centralized source for data on different flavours of disorder in protein structures, MobiDB, building on and expanding the content provided by already existing sources. In addition to the DisProt and PDB X-ray structures, we have added experimental information from NMR structures and five different flavours of two disorder predictors (ESpritz and IUpred). These are combined into a weighted consensus disorder used to classify disordered regions into flexible and constrained disorder. Users are encouraged to submit manual annotations through a submission form. MobiDB features experimental annotations for 17 285 proteins, covering the entire PDB and predictions for the SwissProt database, with 565 200 annotated sequences. Depending on the disorder flavour, 6-20% of the residues are predicted as disordered.

AVAILABILITY

The database is freely available at http://mobidb.bio.unipd.it/.

CONTACT

silvio.tosatto@unipd.it.

ESpritz: accurate and fast prediction of protein disorder

MOTIVATION

Intrinsically disordered regions are key for the function of numerous proteins, and the scant available experimental annotations suggest the existence of different disorder flavors. While efficient predictions are required to annotate entire genomes, most existing methods require sequence profiles for disorder prediction, making them cumbersome for high-throughput applications.

RESULTS

In this work, we present an ensemble of protein disorder predictors called ESpritz. These are based on bidirectional recursive neural networks and trained on three different flavors of disorder, including a novel NMR flexibility predictor. ESpritz can produce fast and accurate sequence-only predictions, annotating entire genomes in the order of hours on a single processor core. Alternatively, a slower but slightly more accurate ESpritz variant using sequence profiles can be used for applications requiring maximum performance. Two levels of prediction confidence allow either to maximize reasonable disorder detection or to limit expected false positives to 5%. ESpritz performs consistently well on the recent CASP9 data, reaching a S(w) measure of 54.82 and area under the receiver operator curve of 0.856. The fast predictor is four orders of magnitude faster and remains better than most publicly available CASP9 methods, making it ideal for genomic scale predictions.

CONCLUSIONS

ESpritz predicts three flavors of disorder at two distinct false positive rates, either with a fast or slower and slightly more accurate approach. Given its state-of-the-art performance, it can be especially useful for high-throughput applications.

AVAILABILITY

Both a web server for high-throughput analysis and a Linux executable version of ESpritz are available from: http://protein.bio.unipd.it/espritz/.