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Sarco(endo)plasmic reticulum calcium (Ca 2+ ) ATPase (SERCA) transports Ca 2+ in muscle. Impaired SERCA activity may contribute to diabetic myopathy. Sirtuin (SIRT) 3 regulates muscle metabolism and function; however, it is unknown if SIRT3 regulates muscle SERCA activity or acetylation. We determined if SIRT3 overexpression enhances SERCA activity in mouse gastrocnemius muscle and if SIRT3 overexpression preserves gastrocnemius SERCA activity in a model of type 2 diabetes, induced by high fat - high sucrose (HFHS) feeding. We also determined if the acetylation status of SERCA proteins in mouse gastrocnemius is altered by SIRT3 overexpression or HFHS feeding. Wild-type (WT) and SIRT3 transgenic (SIRT3 TG ) mice, overexpressing SIRT3 in skeletal muscle, were fed a standard or HFHS diet for 4 months. SIRT3 TG and WT mice developed obesity and glucose intolerance after 4 months of HFHS feeding. SERCA V max was higher in gastrocnemius of SIRT3 TG mice compared with WT mice. HFHS-fed mice had lower SERCA1a protein levels and lower SERCA V max in their gastrocnemius than control-fed mice. The decrease in SERCA V max in gastrocnemius muscle due to HFHS feeding was attenuated by SIRT3 overexpression in HFHS-fed SIRT3 TG mice. SERCA1a and SERCA2a acetylation in mouse gastrocnemius was not altered by genotype or diet. These findings suggest SIRT3 overexpression improves SERCA function in mouse skeletal muscle.

Although RNA-binding proteins (RBPs) are known to be enriched in intrinsic disorder, no previous analysis focused on RBPs interacting with specific RNA types. We fill this gap with a comprehensive analysis of the putative disorder in RBPs binding to six common RNA types: messenger RNA (mRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), and internal ribosome RNA (irRNA). We also analyze the amount of putative intrinsic disorder in the RNA-binding domains (RBDs) and non-RNA-binding-domain regions (non-RBD regions). Consistent with previous studies, we show that in comparison with human proteome, RBPs are significantly enriched in disorder. However, closer examination finds significant enrichment in predicted disorder for the mRNA-, rRNA- and snRNA-binding proteins, while the proteins that interact with ncRNA and irRNA are not enriched in disorder, and the tRNA-binding proteins are significantly depleted in disorder. We show a consistent pattern of significant disorder enrichment in the non-RBD regions coupled with low levels of disorder in RBDs, which suggests that disorder is relatively rarely utilized in the RNA-binding regions. Our analysis of the non-RBD regions suggests that disorder harbors posttranslational modification sites and is involved in the putative interactions with DNA. Importantly, we utilize experimental data from DisProt and independent data from Pfam to validate the above observations that rely on the disorder predictions. This study provides new insights into the distribution of disorder across proteins that bind different RNA types and the functional role of disorder in the regions where it is enriched., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

Obesity, type 2 diabetes, and heart disease are linked to an unhealthy diet. Sarco(endo)plasmic reticulum calcium (Ca 2+ ) ATPase 2a (SERCA2a) controls cardiac function by transporting Ca 2+ in cardiomyocytes. SERCA2a is altered by diet and acetylation, independently; however, it is unknown if diet alters cardiac SERCA2a acetylation. Sirtuin (SIRT) 3 is an enzyme that might preserve health under conditions of macronutrient excess by modulating metabolism via regulating deacetylation of target proteins. Our objectives were to determine if muscle-specific SIRT3 overexpression attenuates the pathological effects of high fat-high sucrose (HFHS) feeding and if HFHS feeding alters cardiac SERCA2a acetylation. We also determined if SIRT3 alters cardiac SERCA2a acetylation and regulates cardiac SERCA2a activity. C57BL/6J wild-type (WT) mice and MCK-mSIRT3-M1-Flag transgenic (SIRT3 TG ) mice, overexpressing SIRT3 in cardiac and skeletal muscle, were fed a standard-diet or a HFHS-diet for 4 months. SIRT3 TG and WT mice developed obesity, glucose intolerance, cardiac dysfunction, and pathological cardiac remodeling after 4 months of HFHS feeding, indicating muscle-specific SIRT3 overexpression does not attenuate the pathological effects of HFHS-feeding. Overall cardiac lysine acetylation was increased by 63% in HFHS-fed mice (p = 0.022), though HFHS feeding did not alter cardiac SERCA2a acetylation. Cardiac SERCA2a acetylation was not altered by SIRT3 overexpression, whereas SERCA2a V max was 21% higher in SIRT3 TG (p = 0.039) than WT mice. This suggests that SIRT3 overexpression enhanced cardiac SERCA2a activity without direct SERCA2a deacetylation. Muscle-specific SIRT3 overexpression may not prevent the complications associated with an unhealthy diet in mice, but it appears to enhance SERCA2a activity in the mouse heart., (© 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

The recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 1.21 million cases of SARS-CoV-2 infection and more than 67,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. The World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted COVID-19 as a pandemic now. Multiple sequence alignment data correlated with the already published reports on SARS-CoV-2 evolution indicated that this virus is closely related to the bat severe acute respiratory syndrome-like coronavirus (bat SARS-like CoV) and the well-studied human SARS coronavirus (SARS-CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins contains molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of SARS-CoV-2 proteome will have important implications in understanding the structural and non-structural biology of SARS or SARS-like coronaviruses.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown., Results: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding., Conclusions: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure-function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

Frailty is a risk factor for cardiovascular disease (CVD). Biomarkers have the potential to detect the early stages of frailty, such as pre-frailty. Myokines may act as biomarkers of frailty-related disease progression, as a decline in muscle health is a hallmark of the frailty phenotype. This study is a secondary analysis of 104 females 55 years of age or older with no previous history of CVD. Differences in systemic myokine concentrations based on frailty status and CVD risk profile were examined using a case-control design. Propensity matching identified two sets of 26 pairs with pre-frailty as the exposure variable in low or elevated CVD risk groups for a total 104 female participants. Frailty was assessed using the Fried Criteria (FC) and CVD risk was assessed using the Framingham Risk Score (FRS). Factorial ANOVA compared the main effects of frailty, CVD risk, and their interaction on the concentrations of 15 myokines. Differences were found when comparing elevated CVD risk status with low for the concentrations of EPO (384.76 ± 1046.07 vs. 206.63 ± 284.61 pg/mL, p = .001), FABP3 (2772.61 ± 3297.86 vs. 1693.31 ± 1019.34 pg/mL, p = .017), FGF21 (193.17 ± 521.09 vs. 70.18 ± 139.51 pg/mL, p = .010), IL-6 (1.73 ± 4.97 vs. 0.52 ± 0.89 pg/mL, p = .023), and IL-15 (2.62 ± 10.56 vs. 0.92 ± 1.25 pg/mL, p = .022). Pre-frail females had lower concentrations of fractalkine compared to robust (27.04 ± 20.60 vs. 103.62 ± 315.45 pg/mL, p = .004). Interaction effects between frailty status and CVD risk for FGF21 and OSM were identified. In elevated CVD risk, pre-frail females, concentrations of FGF21 and OSM were lower than that of elevated CVD risk, robust females (69.10 ± 62.86 vs. 317.24 ± 719.69, p = .011; 1.73 ± 2.32 vs. 24.43 ± 69.21, p = .018, respectively). These data identified specific biomarkers of CVD risk and biomarkers of frailty that are exacerbated with CVD risk., Competing Interests: Declaration of competing interest The authors declare no conflicting interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The heart is capable of responding to stressful situations by increasing muscle mass, which is broadly defined as cardiac hypertrophy. This phenomenon minimizes ventricular wall stress for the heart undergoing a greater than normal workload. At initial stages, cardiac hypertrophy is associated with normal or enhanced cardiac function and is considered to be adaptive or physiological; however, at later stages, if the stimulus is not removed, it is associated with contractile dysfunction and is termed as pathological cardiac hypertrophy. It is during physiological cardiac hypertrophy where the function of subcellular organelles, including the sarcolemma, sarcoplasmic reticulum, mitochondria, and myofibrils, may be upregulated, while pathological cardiac hypertrophy is associated with downregulation of these subcellular activities. The transition of physiological cardiac hypertrophy to pathological cardiac hypertrophy may be due to the reduction in blood supply to hypertrophied myocardium as a consequence of reduced capillary density. Oxidative stress, inflammatory processes, Ca 2+ -handling abnormalities, and apoptosis in cardiomyocytes are suggested to play a critical role in the depression of contractile function during the development of pathological hypertrophy.

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

Protein-coding nucleic acids exhibit composition and codon biases between sequences coding for intrinsically disordered regions (IDRs) and those coding for structured regions. IDRs are regions of proteins that are folding self-insufficient and which function without the prerequisite of folded structure. Several authors have investigated composition bias or codon selection in regions encoding for IDRs, primarily in Eukaryota, and concluded that elevated GC content is the result of the biased amino acid composition of IDRs. We substantively extend previous work by examining GC content in regions encoding IDRs, from 44 species in Eukaryota, Archaea, and Bacteria, spanning a wide range of GC content. We confirm that regions coding for IDRs show a significantly elevated GC content, even across all domains of life. Although this is largely attributable to the amino acid composition bias of IDRs, we show that this bias is independent of the overall GC content and, most importantly, we are the first to observe that GC content bias in IDRs is significantly different than expected from IDR amino acid composition alone. We empirically find compensatory codon selection that reduces the observed GC content bias in IDRs. This selection is dependent on the overall GC content of the organism. The codon selection bias manifests as use of infrequent, AT-rich codons in encoding IDRs. Further, we find these relationships to be independent of the intrinsic disorder prediction method used, and independent of estimated translation efficiency. These observations are consistent with the previous work, and we speculate on whether the observed biases are causal or symptomatic of other driving forces.

Disordered binding regions (DBRs), which are embedded within intrinsically disordered proteins or regions (IDPs or IDRs), enable IDPs or IDRs to mediate multiple protein-protein interactions. DBR-protein complexes were collected from the Protein Data Bank for which two or more DBRs having different amino acid sequences bind to the same (100% sequence identical) globular protein partner, a type of interaction herein called many-to-one binding. Two distinct binding profiles were identified: independent and overlapping. For the overlapping binding profiles, the distinct DBRs interact by means of almost identical binding sites (herein called "similar"), or the binding sites contain both common and divergent interaction residues (herein called "intersecting"). Further analysis of the sequence and structural differences among these three groups indicate how IDP flexibility allows different segments to adjust to similar, intersecting, and independent binding pockets.

RNA chaperone activity is one of the many functions of intrinsically disordered regions (IDRs). IDRs function without the prerequisite of a stable structure. Instead, their functions arise from structural ensembles. A common theme in IDR function is molecular recognition; IDRs mediate interactions with other proteins, RNA, and DNA. Many computational methods are available to predict IDRs from protein sequence, but relatively few are available for predicting IDR functions. Available methods primarily focus on protein-protein interactions. DisoRDPbind was developed to predict several protein functions including interactions with RNA. This method is available as a user-friendly web interface, located at http://biomine.cs.vcu.edu/servers/DisoRDPbind/ . The development and architecture of DisoRDPbind is briefly presented, and its accuracy relative to other RNA-binding residue predictors is discussed. We explain usage of the web interface in detail and provide an example of prediction results and interpretation. While DisoRDPbind does not identify RNA chaperones directly, we provide a case study of an RNA chaperone, HCV core protein, as an example of the method's utility in the study of RNA chaperones.

Intrinsically disorder regions (IDRs) lack a stable structure, yet perform biological functions. The functions of IDRs include mediating interactions with other molecules, including proteins, DNA, or RNA and entropic functions, including domain linkers. Computational predictors provide residue-level indications of function for disordered proteins, which contrasts with the need to functionally annotate the thousands of experimentally and computationally discovered IDRs. In this work, we investigate the feasibility of using residue-level prediction methods for region-level function predictions. For an initial examination of the multiple function region-level prediction problem, we constructed a dataset of (likely) single function IDRs in proteins that are dissimilar to the training datasets of the residue-level function predictors. We find that available residue-level prediction methods are only modestly useful in predicting multiple region-level functions. Classification is enhanced by simultaneous use of multiple residue-level function predictions and is further improved by inclusion of amino acids content extracted from the protein sequence. We conclude that multifunction prediction for IDRs is feasible and benefits from the results produced by current residue-level function predictors, however, it has to accommodate inaccuracy in functional annotations.

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

The intense interest in the intrinsically disordered proteins in the life science community, together with the remarkable advancements in predictive technologies, have given rise to the development of a large number of computational predictors of intrinsic disorder from protein sequence. While the growing number of predictors is a positive trend, we have observed a considerable difference in predictive quality among predictors for individual proteins. Furthermore, variable predictor performance is often inconsistent between predictors for different proteins, and the predictor that shows the best predictive performance depends on the unique properties of each protein sequence. We propose a computational approach, DISOselect, to estimate the predictive performance of 12 selected predictors for individual proteins based on their unique sequence-derived properties. This estimation informs the users about the expected predictive quality for a selected disorder predictor and can be used to recommend methods that are likely to provide the best quality predictions. Our solution does not depend on the results of any disorder predictor; the estimations are made based solely on the protein sequence. Our solution significantly improves predictive performance, as judged with a test set of 1,000 proteins, when compared to other alternatives. We have empirically shown that by using the recommended methods the overall predictive performance for a given set of proteins can be improved by a statistically significant margin. DISOselect is freely available for non-commercial users through the webserver at http://biomine.cs.vcu.edu/servers/DISOselect/., (© 2019 The Protein Society.)

The wait before elective cardiac intervention or surgery presents an opportunity to prevent further physiologic decline preoperatively in older patients. Implementation of prehabilitation programs decreases length of hospital stay postoperatively, decreases time spent in the intensive care unit, decreases postoperative complications, and improves self-reported quality of life postsurgery. Prehabilitation programs should adopt multimodal approaches including nutrition, exercise, and worry reduction to improve patient resilience in the preoperative period. High-quality research in larger cohorts is needed, and interventions focusing on underrepresented frailer populations and women. Creative ways to improve accessibility, adherence, and benefits received from prehabilitation should be explored., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein-protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence-disorder-function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ➔ disorder ensemble ➔ function paradigm., (© 2019 The Protein Society.)

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., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Intrinsically disordered proteins and regions are involved in a wide range of cellular functions, and they often facilitate protein-protein interactions. Molecular recognition features (MoRFs) are segments of intrinsically disordered regions that bind to partner proteins, where binding is concomitant with a transition to a structured conformation. MoRFs facilitate translation, transport, signaling, and regulatory processes and are found across all domains of life. A popular computational tool, MoRFpred, accurately predicts MoRFs in protein sequences. MoRFpred is implemented as a user-friendly web server that is freely available at http://biomine.cs.vcu.edu/servers/MoRFpred/ . We describe this predictor, explain how to run the web server, and show how to interpret the results it generates. We also demonstrate the utility of this web server based on two case studies, focusing on the relevance of evolutionary conservation of MoRF regions.

Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.

It is recognized now that various proteinaceous membrane-less organelles (PMLOs) are commonly found in cytoplasm, nucleus, and mitochondria of various eukaryotic cells (as well as in the chloroplasts of plant cells). Being different from the "traditional" membrane-encapsulated organelles, such as chloroplasts, endoplasmic reticulum, Golgi apparatus, lysosomes, mitochondria, nucleus, and vacuoles, PMLOs solve the cellular need to facilitate and regulate molecular interactions via reversible and controllable isolation of target molecules in specialized compartments. PMLOs possess liquid-like behavior and are believed to be formed as a result of biological liquid-liquid phase transitions (LLPTs, also known as liquid-liquid phase separation), where an intricate interplay between RNA and intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains and intrinsically disordered protein regions (IDPRs) may play an important role. This review analyzes the prevalence of intrinsic disorder in proteins associated with various PMLOs found in human cells and considers some of the functional roles of IDPs/IDPRs in biogenesis of these organelles., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Studies of diverse phylogenetic lineages reveal that protein disorder increases in concert with organismic complexity but that differences nevertheless exist among lineages. To gain insight into this phenomenology, we analyzed all of the transcription factor (TF) families for which sequences are known for 17 species spanning bacteria, yeast, algae, land plants, and animals and for which the number of different cell types has been reported in the primary literature. Although the fraction of disordered residues in TF sequences is often moderately or poorly correlated with organismic complexity as gauged by cell-type number (r2 < 0.5), an unbiased and phylogenetically broad analysis shows that organismic complexity is positively and strongly correlated with the total number of TFs, the number of their spliced variants and their total disordered residues content (r2 > 0.8). Furthermore, the correlation between the fraction of disordered residues and cell-type number becomes stronger when confined to the TF families participating in cell cycle, cell size, cell division, cell differentiation, or cell proliferation, and other important developmental processes. The data also indicate that evolutionarily simpler organisms allow for the detection of subtle differences in the conserved IDRs of TFs as well as changes in variable IDRs, which can influence the DNA recognition and multifunctionality of TFs through direct or indirect mechanisms. Although strong correlations cannot be taken as evidence for cause-and-effect relationships, we interpret our data to indicate that increasing TF disorder likely was an important factor contributing to the evolution of organismic complexity and not merely a concurrent unrelated effect of increasing organismic complexity., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

From the 1970s to the present, regions of missing electron density in protein structures determined by X-ray diffraction and the characterization of the functions of these regions have suggested that not all protein regions depend on prior 3D structure to carry out function. Motivated by these observations, in early 1996 we began to use bioinformatics approaches to study these intrinsically disordered proteins (IDPs) and IDP regions. At just about the same time, several laboratory groups began to study a collection of IDPs and IDP regions using nuclear magnetic resonance. The temporal overlap of the bioinformatics and NMR studies played a significant role in the development of our understanding of IDPs. Here the goal is to recount some of this history and to project from this experience possible directions for future work.

Intrinsically disordered proteins (IDPs) and IDP regions lack stable tertiary structure yet carry out numerous biological functions, especially those associated with signaling, transcription regulation, DNA condensation, cell division, and cellular differentiation. Both post-translational modifications (PTMs) and alternative splicing (AS) expand the functional repertoire of IDPs. Here we propose that an "IDP-based developmental toolkit," which is comprised of IDP regions, PTMs, especially multiple PTMs, within these IDP regions, and AS events within segments of pre-mRNA that code for these same IDP regions, allows functional diversification and environmental responsiveness for molecules that direct the development of complex metazoans., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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.

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.

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.

Molecular recognition features (MoRFs) are intrinsically disordered protein regions that bind to partners via disorder-to-order transitions. In one-to-many binding, a single MoRF binds to two or more different partners individually. MoRF-based one-to-many protein-protein interaction (PPI) examples were collected from the Protein Data Bank, yielding 23 MoRFs bound to 2-9 partners, with all pairs of same-MoRF partners having less than 25% sequence identity. Of these, 8 MoRFs were bound to 2-9 partners having completely different folds, whereas 15 MoRFs were bound to 2-5 partners having the same folds but with low sequence identities. For both types of partner variation, backbone and side chain torsion angle rotations were used to bring about the conformational changes needed to enable close fits between a single MoRF and distinct partners. Alternative splicing events (ASEs) and posttranslational modifications (PTMs) were also found to contribute to distinct partner binding. Because ASEs and PTMs both commonly occur in disordered regions, and because both ASEs and PTMs are often tissue-specific, these data suggest that MoRFs, ASEs, and PTMs may collaborate to alter PPI networks in different cell types. These data enlarge the set of carefully studied MoRFs that use inherent flexibility and that also use ASE-based and/or PTM-based surface modifications to enable the same disordered segment to selectively associate with two or more partners. The small number of residues involved in MoRFs and in their modifications by ASEs or PTMs may simplify the evolvability of signaling network diversity., (Copyright © 2013 The Protein Society.)

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The identification of intrinsically disordered proteins (IDPs) among the targets that fail to form satisfactory crystal structures in the Protein Structure Initiative represents a key to reducing the costs and time for determining three-dimensional structures of proteins. To help in this endeavor, several Protein Structure Initiative Centers were asked to send samples of both crystallizable proteins and proteins that failed to crystallize. The abundance of intrinsic disorder in these proteins was evaluated via computational analysis using predictors of natural disordered regions (PONDR®) and the potential cleavage sites and corresponding fragments were determined. Then, the target proteins were analyzed for intrinsic disorder by their resistance to limited proteolysis. The rates of tryptic digestion of sample target proteins were compared to those of lysozyme/myoglobin, apomyoglobin, and α-casein as standards of ordered, partially disordered and completely disordered proteins, respectively. At the next stage, the protein samples were subjected to both far-UV and near-UV circular dichroism (CD) analysis. For most of the samples, a good agreement between CD data, predictions of disorder and the rates of limited tryptic digestion was established. Further experimentation is being performed on a smaller subset of these samples in order to obtain more detailed information on the ordered/disordered nature of the proteins., (Copyright © 2012 Elsevier Inc. All rights reserved.)

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.

Induced pluripotent stem (iPS) cells can be obtained from terminally differentiated somatic cells by overexpression of defined sets of reprogramming transcription factors. These protein sets have been called the Yamanaka factors, namely Sox2, Oct3/4 (Pou5f1), Klf4, and c-Myc, and the Thomson factors, namely Sox2, Oct3, Lin28, and Nanog. Other sets of proteins, while not essential for the formation of iPS cells, are important for improving the efficiency of the induction and still other sets of proteins are important as markers for embryonic stem cells. Structural information about most of these important proteins is very sparse. Our bioinformatics analysis herein reveals that these reprogramming factors and most of the efficiency-improving and embryonic stem cell markers are highly enriched in intrinsic disorder. As is typical for transcription factors, these proteins are modular. Specific sites for interaction with other proteins and DNA are dispersed in the long regions of intrinsic disorder. These highly dynamic interaction sites are evidently responsible for the delicate interplay among various molecules. The bioinformatics analysis given herein should facilitate the investigation of the roles and organization of these modular interaction sites, thereby helping to shed further light on the pathways that underlie the mechanism(s) by which terminally differentiated cells are converted to iPS cells.

Many proteins or their regions are disordered in their native, biologically active states. Bioinformatics has revealed that these proteins/regions are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition, signal transduction, protein-protein, and protein-nucleic acid interactions. Viruses, these "organisms at the edge of life", have uniquely evolved to be highly adaptive for fast change in their biological and physical environment. To sustain these fast environmental changes, viral proteins elaborated multiple measures, from relatively low van der Waals contact densities, to inclusion of a large fraction of residues that are not arranged in well-defined secondary structural elements, to heavy use of short disordered regions, and to high resistance to mutations. On the other hand, viral proteins are rich in intrinsic disorder. Some of the intrinsically disordered regions are heavily used in the functioning of viral proteins. Others likely have evolved to help viruses accommodate to their hostile habitats. Still others evolved to help viruses in managing their economic usage of genetic material via alternative splicing, overlapping genes, and anti-sense transcription. In this review, we focus on structural peculiarities of viral proteins and on the role of intrinsic disorder in their functions.

Background: Many proteins or their regions known as intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack unique 3D structure in their native states under physiological conditions yet fulfill key biological functions. Earlier bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. Archaea belong to an intriguing domain of life whose members, being microbes, are characterized by a unique mosaic-like combination of bacterial and eukaryotic properties and include inhabitants of some of the most extreme environments on the planet. With the expansion of the archaea genome data (more than fifty archaea species from five different phyla are known now), and with recent improvements in the accuracy of intrinsic disorder prediction, it is time to re-examine the abundance of IDPs and IDRs in the archaea domain., Results: The abundance of IDPs and IDRs in 53 archaea species is analyzed. The amino acid composition profiles of these species are generally quite different from each other. The disordered content is highly species-dependent. Thermoproteales proteomes have 14% of disordered residues, while in Halobacteria, this value increases to 34%. In proteomes of these two phyla, proteins containing long disordered regions account for 12% and 46%, whereas 4% and 26% their proteins are wholly disordered. These three measures of disorder content are linearly correlated with each other at the genome level. There is a weak correlation between the environmental factors (such as salinity, pH and temperature of the habitats) and the abundance of intrinsic disorder in Archaea, with various environmental factors possessing different disorder-promoting strengths. Harsh environmental conditions, especially those combining several hostile factors, clearly favor increased disorder content. Intrinsic disorder is highly abundant in functional Pfam domains of the archaea origin. The analysis based on the disordered content and phylogenetic tree indicated diverse evolution of intrinsic disorder among various classes and species of Archaea., Conclusions: Archaea proteins are rich in intrinsic disorder. Some of these IDPs and IDRs likely evolve to help archaea to accommodate to their hostile habitats. Other archaean IDPs and IDRs possess crucial biological functions similar to those of the bacterial and eukaryotic IDPs/IDRs.

The plant growth-repressing DELLA proteins (DELLAs) are known to represent a convergence point in integration of multiple developmental and environmental signals in planta, one of which is hormone gibberellic acid (GA). Binding of the liganded GA receptor (GID1/GA) to the N-terminal domain of DELLAs is required for GA-induced degradation of DELLAs via the ubiquitin-proteasome pathway, thus derepressing plant growth. However, the conformational changes of DELLAs upon binding to GID1/GA, which are the key to understanding the precise mechanism of GID1/GA-mediated degradation of DELLAs, remain unclear. Using biophysical, biochemical, and bioinformatics approaches, we demonstrated for the first time that the unbound N-terminal domains of DELLAs are intrinsically unstructured proteins under physiological conditions. Within the intrinsically disordered N-terminal domain of DELLAs, we have identified several molecular recognition features, sequences known to undergo disorder-to-order transitions upon binding to interacting proteins in intrinsically unstructured proteins. In accordance with the molecular recognition feature analyses, we have observed the binding-induced folding of N-terminal domains of DELLAs upon interaction with AtGID1/GA. Our results also indicate that DELLA proteins can be divided into two subgroups in terms of their molecular compactness and their interactions with monoclonal antibodies.

Background: Intrinsically disordered proteins lack stable structure under physiological conditions, yet carry out many crucial biological functions, especially functions associated with regulation, recognition, signaling and control. Recently, human genetic diseases and related genes were organized into a bipartite graph (Goh KI, Cusick ME, Valle D, Childs B, Vidal M, et al. (2007) The human disease network. Proc Natl Acad Sci U S A 104: 8685-8690). This diseasome network revealed several significant features such as the common genetic origin of many diseases., Methods and Findings: We analyzed the abundance of intrinsic disorder in these diseasome network proteins by means of several prediction algorithms, and we analyzed the functional repertoires of these proteins based on prior studies relating disorder to function. Our analyses revealed that (i) Intrinsic disorder is common in proteins associated with many human genetic diseases; (ii) Different disease classes vary in the IDP contents of their associated proteins; (iii) Molecular recognition features, which are relatively short loosely structured protein regions within mostly disordered sequences and which gain structure upon binding to partners, are common in the diseasome, and their abundance correlates with the intrinsic disorder level; (iv) Some disease classes have a significant fraction of genes affected by alternative splicing, and the alternatively spliced regions in the corresponding proteins are predicted to be highly disordered; and (v) Correlations were found among the various diseasome graph-related properties and intrinsic disorder., Conclusion: These observations provide the basis for the construction of the human-genetic-disease-associated unfoldome.

Background: Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. These activities complement the functions of structured proteins. IDPs and IDRs were shown to participate in both one-to-many and many-to-one signaling. Alternative splicing and posttranslational modifications are frequently used to tune the IDP functionality. Several individual IDPs were shown to be associated with human diseases, such as cancer, cardiovascular disease, amyloidoses, diabetes, neurodegenerative diseases, and others. This raises questions regarding the involvement of IDPs and IDRs in various diseases., Results: IDPs and IDRs were shown to be highly abundant in proteins associated with various human maladies. As the number of IDPs related to various diseases was found to be very large, the concepts of the disease-related unfoldome and unfoldomics were introduced. Novel bioinformatics tools were proposed to populate and characterize the disease-associated unfoldome. Structural characterization of the members of the disease-related unfoldome requires specialized experimental approaches. IDPs possess a number of unique structural and functional features that determine their broad involvement into the pathogenesis of various diseases., Conclusion: Proteins associated with various human diseases are enriched in intrinsic disorder. These disease-associated IDPs and IDRs are real, abundant, diversified, vital, and dynamic. These proteins and regions comprise the disease-related unfoldome, which covers a significant part of the human proteome. Profound association between intrinsic disorder and various human diseases is determined by a set of unique structural and functional characteristics of IDPs and IDRs. Unfoldomics of human diseases utilizes unrivaled bioinformatics and experimental techniques, paves the road for better understanding of human diseases, their pathogenesis and molecular mechanisms, and helps develop new strategies for the analysis of disease-related proteins.

Many biologically active proteins are intrinsically disordered. A reasonable understanding of the disorder status of these proteins may be beneficial for better understanding of their structures and functions. The disorder contents of disordered proteins vary dramatically, with two extremes being fully ordered and fully disordered proteins. Often, it is necessary to perform a binary classification and classify a whole protein as ordered or disordered. Here, an improved error estimation technique was applied to develop the cumulative distribution function (CDF) algorithms for several established disorder predictors. A consensus binary predictor, based on the artificial neural networks, NN-CDF, was developed by using output of the individual CDFs. The consensus method outperforms the individual predictors by 4-5% in the averaged accuracy.

Protein-protein interactions are thought to be mediated by domains, which are autonomous folding units of proteins. Recently, a second type of interaction has been suggested, mediated by short segments termed linear motifs, which are related to recognition elements of intrinsically disordered regions. Here, we propose a third kind of protein-protein recognition mechanism, mediated by disordered regions longer than 20-30 residues. Bioinformatics predictions and well-characterized examples, such as the kinase-inhibitory domain of Cdk inhibitors and the Wiskott-Aldrich syndrome protein (WASP)-homology domain 2 of actin-binding proteins, show that these disordered regions conform to the definition of domains rather than motifs, i.e., they represent functional, evolutionary, and structural units. Their functions are distinct from those of short motifs and ordered domains, and establish a third kind of interaction principle. With these points, we argue that these long disordered regions should be recognized as a distinct class of biologically functional protein domains.

Natural selection shapes the sequence, structure and biophysical properties of proteins to fit their environment. We hypothesize that highly thermostable proteins and viral proteins represent two opposing adaptation strategies. Thermostable proteins are highly compact and possess well-packed hydrophobic cores and intensely charged surfaces. By contrast, viral proteins, and RNA viral proteins in particular, display a high occurrence of disordered segments and loosely packed cores. These features might endow viral proteins with increased structural flexibility and effective ways to interact with the components of the host. They could also be related to high adaptability levels and mutation rates observed in viruses, thus, representing a unique strategy for buffering the deleterious effects of mutations, such that those that have little (interactions), have little to lose.

Intrinsically disordered proteins (IDPs) constitute a recently recognized realm of atypical biologically active proteins that lack stable structure under physiological conditions, but are commonly involved in such crucial cellular processes as regulation, recognition, signaling and control. IDPs are very common among proteins associated with various diseases. Recently, we performed a systematic bioinformatics analysis of the human diseasome, a network that linked the human disease phenome (which includes all the human genetic diseases) with the human disease genome (which contains all the disease-related genes) (Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., and Barabasi, A. L. (2007). The human disease network. Proc. Natl. Acad. Sci. U.S.A. 104, 8685-90). The analysis of this diseasome revealed that IDPs are abundant in proteins linked to human genetic diseases, and that different genetic disease classes varied dramatically in the IDP content (Midic U., Oldfield C.J., Dunker A.K., Obradovic Z., Uversky V.N. (2009) Protein disorder in the human diseasome: Unfoldomics of human genetic diseases. BMC Genomics. In press). Furthermore, many of the genetic disease-related proteins were shown to contain at least one molecular recognition feature, which is a relatively short loosely structured protein region within a mostly disordered segment with the feature gaining structure upon binding to a partner. Finally, alternative splicing was shown to be abundant among the diseasome genes. Based on these observations the human-genetic-disease-associated unfoldome was created. This minireview describes several illustrative examples of ordered and intrinsically disordered members of the human diseasome.

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.