Proteins can exist in a trinity of structures: the ordered state, the molten globule and the random coil. Five examples follow which suggest that native protein structure can correspond to any of the three states (not just the ordered state) and that protein function can arise from any of the three states and their transitions. 1. In a process that likely mimics infection, fd phage converts from the ordered into the disordered molten globular state. 2. Nucleosome hyperacetylation is crucial to DNA replication and transcription; this chemical modification greatly increases the net negative charge of the nucleosome core particle. We propose that the increased charge imbalance promotes its conversion to a much less rigid form. 3. Clusterin contains an ordered domain and also a native molten globular region. The molten globular domain likely functions as a proteinaceous detergent for cell remodeling and removal of apoptotic debris. 4. In a critical signaling event, a helix in calcineurin becomes bound and surrounded by calmodulin, thereby turning on calcineurin's serine/threonine phosphatase activity.Locating the calcineurin helix within a region of disorder is essential for enabling calmodulin to surround its target upon binding. 5. Calsequestrin regulates calcium levels in the sarcoplasmic reticulum by binding about 50 ions/molecule. Disordered polyanion tails at the carboxy terminus bind many of these calcium ions, perhaps without adopting a unique structure. In addition to these examples, 16 more proteins with native disorder will be discussed. These disordered regions include molecular recognition domains, protein folding inhibitors, flexible linkers, entropic springs, entropic clocks and entropic bristles.Motivated by such examples of intrinsic disorder, we are studying the relationships between amino acid sequence and order/disorder, and from this information we are predicting intrinsic order/disorder from amino acid sequence.The sequence/structure relationships indicate that disorder is an encoded property, and the predictions strongly suggest that proteins in nature are much richer in intrinsic disorder than are those in the Protein Data Bank. Recent predictions on 29 genomes indicate that proteins from eucaryotes apparently have more intrinsic disorder than those from either bacteria or archaea, with typically > 30 % of eucaryotic proteins having disordered regions of length = 50 consecutive residues.

The dominant view of protein structure-function is that an amino acid sequence specifies a (mostly) fixed three-dimensional (3-D) structure that is a prerequisite to protein function. In contrast to the dominant view, many proteins display functions requiring the disordered state. Our purpose here is to provide a catalogue of disorder-function relationships. The very important molecular details in each example can be obtained from the references provided or from several excellent reviews and commentaries (1-9).For ordered protein, the ensemble members all have the same time-averaged canonical set of Ramachandran angles along their backbones. For intrinsically disordered protein, the ensemble members have different (and typically dynamic) Ramachandran angles. Such disorder has been characterized by a variety of methods including x-ray crystallography, NMR spectroscopy, CD spectroscopy, and protease sensitivity to name several. Each of these methods has advantages and limitations that are discussed in more detail elsewhere (10). Although a few disordered proteins and regions have been characterized by several methods as noted below, it would be useful to have more examples with multiple methods of characterization.In attempts to discover generalities from the known disorder examples, we recently used bioinformatics coupled with data mining (11)(12)(13)(14)(15). The results suggested that thousands of natively disordered proteins exist, representing a very substantial fraction of the proteins in the commonly used sequence databases (13,16). From these and related database predictions and from a set of functionally important disordered proteins, Wright and Dyson (17) called for a reassessment of the view that 3-D structure is always a prerequisite to protein function.In this article, we discuss the following topics: 1. how common is intrinsic disorder?; 2. intrinsic disorder in vivo; 3. functional annotations for 90 proteins having physically characterized regions of disorder; 4. disordered regions without known function 5. a structurefunction proposal called "the protein trinity"; 6. the functional repertoires of ordered and disordered protein, and 7. the need for a Disordered Protein Database (DisProt) to complement the Protein Data Bank (PDB).How Common is Intrinsic Disorder? A series of predictors of natural disordered regions (PONDRs) have been developed using amino acid sequence as inputs and giving intrinsic order or disorder tendencies as outputs (11,14,15,18,19). The various PONDRs are distinguished by different training sets, by different data representations for their inputs, and by different machine learning models for their development.For PONDR VL-XT 1 , currently the best characterized of the PONDRs, only 6% of more than 900 non-homologous proteins spanning PDB gave false positive predictions of disorder ≥ 40 consecutive amino acids in length. Even this 6% may be an over-estimate of the false positive error rate, however, because many of these predicted disordered regions are involved in ligand bindin...

Intrinsic disorder refers to segments or to whole proteins that fail to self-fold into fixed 3D structure, with such disorder sometimes existing in the native state. Here we report data on the relationships among intrinsic disorder, sequence complexity as measured by Shannon's entropy, and amino acid composition. Intrinsic disorder identified in protein crystal structures, and by nuclear magnetic resonance, circular dichroism, and prediction from amino acid sequence, all exhibit similar complexity distributions that are shifted to lower values compared to, but significantly overlapping with, the distribution for ordered proteins. Compared to sequences from ordered proteins, these variously characterized intrinsically disordered segments and proteins, and also a collection of lowcomplexity sequences, typically have obviously higher levels of protein-specific subsets of the following amino acids: R, K, E, P, and S, and lower levels of subsets of the following: C, W, Y, I, and V. The Swiss Protein database of sequences exhibits significantly higher amounts of both low-complexity and predicted-to-be-disordered segments as compared to a non-redundant set of sequences from the Protein Data Bank, providing additional data that nature is richer in disordered and low-complexity segments compared to the commonness of these features in the set of structurally characterized proteins.

Reversible protein phosphorylation provides a major regulatory mechanism in eukaryotic cells. Due to the high variability of amino acid residues flanking a relatively limited number of experimentally identified phosphorylation sites, reliable prediction of such sites still remains an important issue. Here we report the development of a new web-based tool for the prediction of protein phosphorylation sites, DISPHOS (DISorder-enhanced PHOSphorylation predictor, http://www.ist.temple. edu/DISPHOS). We observed that amino acid compositions, sequence complexity, hydrophobicity, charge and other sequence attributes of regions adjacent to phosphorylation sites are very similar to those of intrinsically disordered protein regions. Thus, DISPHOS uses position-specific amino acid frequencies and disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites. Based on the estimates of phosphorylation rates in various protein categories, the outputs of DISPHOS are adjusted in order to reduce the total number of misclassified residues. When tested on an equal number of phosphorylated and non-phosphorylated residues, the accuracy of DISPHOS reaches 76% for serine, 81% for threonine and 83% for tyrosine. The significant enrichment in disorder-promoting residues surrounding phosphorylation sites together with the results obtained by applying DISPHOS to various protein functional classes and proteomes, provide strong support for the hypothesis that protein phosphorylation predominantly occurs within intrinsically disordered protein regions.

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The Database of Protein Disorder (DisProt) links structure and function information for intrinsically disordered proteins (IDPs). Intrinsically disordered proteins do not form a fixed three-dimensional structure under physiological conditions, either in their entireties or in segments or regions. We define IDP as a protein that contains at least one experimentally determined disordered region. Although lacking fixed structure, IDPs and regions carry out important biological functions, being typically involved in regulation, signaling and control. Such functions can involve high-specificity low-affinity interactions, the multiple binding of one protein to many partners and the multiple binding of many proteins to one partner. These three features are all enabled and enhanced by protein intrinsic disorder. One of the major hindrances in the study of IDPs has been the lack of organized information. DisProt was developed to enable IDP research by collecting and organizing knowledge regarding the experimental characterization and the functional associations of IDPs. In addition to being a unique source of biological information, DisProt opens doors for a plethora of bioinformatics studies. DisProt is openly available at .

The recent advances in the prediction of intrinsically disordered proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are reviewed here, especially with regard to protein function. First, a close look is taken at intrinsically disordered proteins and then at the methods used for their experimental characterization. Next, the major statistical properties of disordered regions are summarized, and prediction models developed thus far are described, including their numerous applications in functional proteomics. The future of the prediction of protein disorder and the future uses of such predictions in functional proteomics comprise the last section of this article.

BackgroundDue to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.ResultsWe proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (≤30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder.ConclusionThe VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at