Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.

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.

During the production of our manuscript [1], an incorrect Figure ​Figure11 was put in place of the one we submitted, and we erred in not appropriately informing the production staff of this mistake. The corrected figure and the text describing this figure, as well as the figure legend, are present herein. Figure 1. Charge-Hydropathy plots. In (A) the IDP-Hydropathy scale was used, in (B) the Guy (1985) Hydropathy scale was used, and in (C) the Kyte-Doolittle (1981) hydropathy scale was used. Red circles indicate disordered proteins, blue circles indicate structured ... As stated in our manuscript [1], “The C-H plots generated using scale SVM parameters scale, Kyte-Doolittle hydropathy scale, and Guy hydropathy scale for whole protein prediction are shown in Figure ​Figure1.1. Figure ​Figure1A,1A, which is derived by SVM parameters scale, shows many fewer misclassified disordered proteins on the ordered side, compared to Figure ​Figure1B1B and ​and1C1C.” As further stated in reference [1]: “Figure 1. Charge-Hydropathy plots. In (A) the IDP-Hydropathy scale was used, in (B) the Guy (1985) Hydropathy scale was used, and in (C) the Kyte-Doolittle (1981) hydropathy scale was used. Red circles indicate disordered proteins, blue circles indicate structured proteins. For these plots, each scale was normalized to be in the interval of 0 to 1. The Guy’s scale is multiplied by -1 prior to normalization to conform to the energy rule set by Kyte-Doolittle scale. In (A) the function describing the boundary is:  = 3.31  -0.97. In (B) the function describing the boundary is:  = 2.32  -0.93. In (C), the function describing the boundary is  = 1.35  -0.49.” For more details, the reader is referred to the published manuscript [1].

BACKGROUND: The earliest whole protein order/disorder predictor (Uversky et al., Proteins, 41: 415-427 (2000)), herein called the charge-hydropathy (C-H) plot, was originally developed using the Kyte-Doolittle (1982) hydropathy scale (Kyte & Doolittle., J. Mol. Biol, 157: 105-132(1982)). Here the goal is to determine whether the performance of the C-H plot in separating structured and disordered proteins can be improved by using an alternative hydropathy scale. RESULTS: Using the performance of the CH-plot as the metric, we compared 19 alternative hydropathy scales, with the finding that the Guy (1985) hydropathy scale (Guy, Biophys. J, 47:61-70(1985)) was the best of the tested hydropathy scales for separating large collections structured proteins and intrinsically disordered proteins (IDPs) on the C-H plot. Next, we developed a new scale, named IDP-Hydropathy, which further improves the discrimination between structured proteins and IDPs. Applying the C-H plot to a dataset containing 109 IDPs and 563 non-homologous fully structured proteins, the Kyte-Doolittle (1982) hydropathy scale, the Guy (1985) hydropathy scale, and the IDP-Hydropathy scale gave balanced two-state classification accuracies of 79%, 84%, and 90%, respectively, indicating a very substantial overall improvement is obtained by using different hydropathy scales. A correlation study shows that IDP-Hydropathy is strongly correlated with other hydropathy scales, thus suggesting that IDP-Hydropathy probably has only minor contributions from amino acid properties other than hydropathy. CONCLUSION: We suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.

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.

The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7-2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.

The Mediator complex provides an interface between gene-specific regulatory proteins and the general transcription machinery including RNA polymerase II (RNAP II). The complex has a modular architecture (Head, Middle, and Tail) and cryoelectron microscopy analysis suggested that it undergoes dramatic conformational changes upon interactions with activators and RNAP II. These rearrangements have been proposed to play a role in the assembly of the preinitiation complex and also to contribute to the regulatory mechanism of Mediator. In analogy to many regulatory and transcriptional proteins, we reasoned that Mediator might also utilize intrinsically disordered regions (IDRs) to facilitate structural transitions and transmit transcriptional signals. Indeed, a high prevalence of IDRs was found in various subunits of Mediator from both Saccharomyces cerevisiae and Homo sapiens, especially in the Tail and the Middle modules. The level of disorder increases from yeast to man, although in both organisms it significantly exceeds that of multiprotein complexes of a similar size. IDRs can contribute to Mediator's function in three different ways: they can individually serve as target sites for multiple partners having distinctive structures; they can act as malleable linkers connecting globular domains that impart modular functionality on the complex; and they can also facilitate assembly and disassembly of complexes in response to regulatory signals. Short segments of IDRs, termed molecular recognition features (MoRFs) distinguished by a high protein-protein interaction propensity, were identified in 16 and 19 subunits of the yeast and human Mediator, respectively. In Saccharomyces cerevisiae, the functional roles of 11 MoRFs have been experimentally verified, and those in the Med8/Med18/Med20 and Med7/Med21 complexes were structurally confirmed. Although the Saccharomyces cerevisiae and Homo sapiens Mediator sequences are only weakly conserved, the arrangements of the disordered regions and their embedded interaction sites are quite similar in the two organisms. All of these data suggest an integral role for intrinsic disorder in Mediator's function.

About 10 years ago we published our first predictor of intrinsically disordered protein residues in another IEEE journal, theProceedingsoftheIEEEInternationalConferenceonNeural Networks. Others call such proteins "natively unfolded" and "intrinsically unstructured." Since then, we and others have substantially improved the prediction of intrinsically disordered residues. The prediction of protein intrinsic disorder is similar to the prediction of secondary structure in terms of methodology, but, at the structural level, secondary structure (especially random coil) and intrinsic disorder differ completely in their dynamic motion. First, we will briefly describe the prediction of protein disorder, show the progress from ~ 70 % to ~ 85 % per residue prediction accuracy, and show that intrinsically disordered proteins are common over the three domains of life, but are especially common among the eukaryotes. Next we will discuss our methods for deducing functions that are associated with disordered rather than structured proteins. In brief, structured proteins have advantages for catalysis while disordered proteins and regions have advantages for the reversible, weak binding often observed in signaling, control, and regulation. After that we will discuss how disorder facilitates binding diversity in protein-protein interaction networks, both for single disordered regions binding to many partners and for many disordered regions with different sequences binding to a common site on the surface of one structured protein. Part three presents data indicating that alternative splicing is more prevalent in regions of RNA that code for disorder than those that code for structure, thus providing a means for evolving tissue-specific signaling networks. Finally, we will present a novel approach to drug discovery based on disordered protein.

Just over 10 years ago, in June, 1997, in the Proceedings of the IEEE International Conference on Neural Networks, we published our first predictor of intrinsically disordered protein [1]. Since then, we have substantially improved our predictors, and more than 20 other laboratory groups have joined in efforts to improve the prediction of protein disorder. At the algorithmic level, prediction of protein intrinsic disorder is similar to the prediction of secondary structure, but, at the structural level, secondary structure and intrinsic disorder are entirely different. The secondary structure class called random coil or irregular differs from intrinsic disorder due to very different dynamic properties, with the secondary structure class being much less mobile than the region of disorder. At the biological level, unlike the prediction of secondary structure, the prediction of intrinsic disorder has been revolutionary. That is, for many years, experimentalists have provided evidence that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. Experimentalists further are showing that, for some proteins, functions depended on the unstructured rather than structured state. However, these examples have been mostly ignored. To our knowledge, not one disordered protein or disorder-associated function is discussed in any biochemistry textbook, even though such examples began to be discovered more than 50 years ago. Disorder prediction has been important for showing that the few experimentally characterized examples represent a very large cohort that is found all across all three domains of life.

Intrinsically disordered proteins lack stable tertiary and/or secondary structure under physiological conditions in vitro. They are highly abundant in nature, with ~25-30% of eukaryotic proteins being mostly disordered, and with >50% of eukaryotic proteins and > 70% of signaling proteins having long disordered regions. Functional repertoire of intrinsically disordered proteins is very broad and complements functions of ordered proteins. Often, intrinsically disordered proteins are involved in regulation, signaling and control pathways, where binding to multiple partners and high-speciflcity/low-afflnity interactions play a crucial role. We have found that out of the 711 Swiss-Prot functional keywords associated with at least 20 proteins, 262 were strongly positively correlated with long intrinsically disordered regions, and 302 were strongly negatively correlated. It is suggested that functions of intrinsically disordered proteins may arise from the specific disorder form, from inter-conversion of disordered forms, or from transitions between disordered and ordered conformations. The choice between these conformations is determined by the peculiarities of the protein environment, and many intrinsically disordered proteins possess an exceptional ability to fold in a template-dependent manner. Intrinsically disordered proteins are key players in protein-protein interaction networks being highly abundant among hubs. Furthermore, regions of mRNA which undergo alternative splicing code for disordered proteins much more often than they code for structured proteins. This association of alternative splicing and intrinsic disorder helps proteins to avoid folding difficulties and provides a novel mechanism for developing tissue-specific protein interaction networks. Numerous intrinsically disordered proteins are associated with such human diseases as cancer, cardiovascular disease, amyloidoses, neurodegenerative diseases, diabetes and others. Our bioinformatics analysis revealed that many human diseases are strongly correlated with proteins predicted to be disordered. Contrary to this, we did not find disease associated proteins to be strongly correlated with absence of disorder. Overall, there is an intriguing interconnection between intrinsic disorder, cell signaling and human diseases, which suggests that protein conformational diseases may result not only from protein misfolding, but also from misidentification and missignaling. Intrinsically disordered proteins, such as alpha-synuclein, tau protein, p53, BRCA1 and many other disease-associated hub proteins represent attractive targets for drugs modulating protein-protein interactions. Therefore, novel strategies for drug discovery are based on intrinsically disordered proteins.

Recent proteome-wide screening approaches have provided a wealth of information about interacting proteins in various organisms. To test for a potential association between protein connectivity and the amount of predicted structural disorder, the disorder propensities of proteins with various numbers of interacting partners from four eukaryotic organisms (Caenorhabditis elegans, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens) were investigated. The results of PONDR VL-XT disorder analysis show that for all four studied organisms, hub proteins, defined here as those that interact with > or = 10 partners, are significantly more disordered than end proteins, defined here as those that interact with just one partner. The proportion of predicted disordered residues, the average disorder score, and the number of predicted disordered regions of various lengths were higher overall in hubs than in ends. A binary classification of hubs and ends into ordered and disordered subclasses using the consensus prediction method showed a significant enrichment of wholly disordered proteins and a significant depletion of wholly ordered proteins in hubs relative to ends in worm, fly, and human. The functional annotation of yeast hubs and ends using GO categories and the correlation of these annotations with disorder predictions demonstrate that proteins with regulation, transcription, and development annotations are enriched in disorder, whereas proteins with catalytic activity, transport, and membrane localization annotations are depleted in disorder. The results of this study demonstrate that intrinsic structural disorder is a distinctive and common characteristic of eukaryotic hub proteins, and that disorder may serve as a determinant of protein interactivity.