A. Keith Dunker

Sequence complexity of disordered protein

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 Shannons 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 low‐complexity 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. Proteins 2001;42:38–48.

Intrinsic disorder in cell-signaling and cancer-associated proteins

The number of intrinsically disordered proteins known to be involved in cell-signaling and regulation is growing rapidly. To test for a generalized involvement of intrinsic disorder in signaling and cancer, we applied a neural network predictor of natural disordered regions (PONDR VL-XT) to four protein datasets: human cancer-associated proteins (HCAP), signaling proteins (AfCS), eukaryotic proteins from SWISS-PROT (EU_SW) and non-homologous protein segments with well-defined (ordered) 3D structure (O_PDB_S25). PONDR VL-XT predicts >or=30 consecutive disordered residues for 79(+/-5)%, 66(+/-6)%, 47(+/-4)% and 13(+/-4)% of the proteins from HCAP, AfCS, EU_SW, and O_PDB_S25, respectively, indicating significantly more intrinsic disorder in cancer-associated and signaling proteins as compared to the two control sets. The disorder analysis was extended to 11 additional functionally diverse categories of human proteins from SWISS-PROT. The proteins involved in metabolism, biosynthesis, and degradation together with kinases, inhibitors, transport, G-protein coupled receptors, and membrane proteins are predicted to have at least twofold less disorder than regulatory, cancer-associated and cytoskeletal proteins. In contrast to 44.5% of the proteins from representative non-membrane categories, just 17.3% of the cancer-associated proteins had sequence alignments with structures in the Protein Data Bank covering at least 75% of their lengths. This relative lack of structural information correlated with the greater amount of predicted disorder in the HCAP dataset. A comparison of disorder predictions with the experimental structural data for a subset of the HCAP proteins indicated good agreement between prediction and observation. Our data suggest that intrinsically unstructured proteins play key roles in cell-signaling, regulation and cancer, where coupled folding and binding is a common mechanism.

Flexible nets. The roles of intrinsic disorder in protein interaction networks.

Proteins participate in complex sets of interactions that represent the mechanistic foundation for much of the physiology and function of the cell. These protein–protein interactions are organized into exquisitely complex networks. The architecture of protein–protein interaction networks was recently proposed to be scale‐free, with most of the proteins having only one or two connections but with relatively fewer ‘hubs’ possessing tens, hundreds or more links. The high level of hub connectivity must somehow be reflected in protein structure. What structural quality of hub proteins enables them to interact with large numbers of diverse targets? One possibility would be to employ binding regions that have the ability to bind multiple, structurally diverse partners. This trait can be imparted by the incorporation of intrinsic disorder in one or both partners. To illustrate the value of such contributions, this review examines the roles of intrinsic disorder in protein network architecture. We show that there are three general ways that intrinsic disorder can contribute: First, intrinsic disorder can serve as the structural basis for hub protein promiscuity; secondly, intrinsically disordered proteins can bind to structured hub proteins; and thirdly, intrinsic disorder can provide flexible linkers between functional domains with the linkers enabling mechanisms that facilitate binding diversity. An important research direction will be to determine what fraction of protein–protein interaction in regulatory networks relies on intrinsic disorder.

Intrinsically Disordered Proteins in Human Diseases: Introducing the D2 Concept

Intrinsically disordered proteins (IDPs) lack stable tertiary and/or secondary structures under physiological conditions in vitro. They are highly abundant in nature and their functional repertoire complements the functions of ordered proteins. IDPs are involved in regulation, signaling, and control, where binding to multiple partners and high-specificity/low-affinity interactions play a crucial role. Functions of IDPs are tuned via alternative splicing and posttranslational modifications. Intrinsic disorder is a unique structural feature that enables IDPs to participate in both one-to-many and many-to-one signaling. Numerous IDPs are associated with human diseases, including cancer, cardiovascular disease, amyloidoses, neurodegenerative diseases, and diabetes. Overall, intriguing interconnections among intrinsic disorder, cell signaling, and human diseases suggest that protein conformational diseases may result not only from protein misfolding, but also from misidentification, missignaling, and unnatural or nonnative folding. IDPs, such as alpha-synuclein, tau protein, p53, and BRCA1, are attractive targets for drugs modulating protein-protein interactions. From these and other examples, novel strategies for drug discovery based on IDPs have been developed. To summarize work in this area, we are introducing the D2 (disorder in disorders) concept.

Function and structure of inherently disordered proteins

The application of bioinformatics methodologies to proteins inherently lacking 3D structure has brought increased attention to these macromolecules. Here topics concerning these proteins are discussed, including their prediction from amino acid sequence, their enrichment in eukaryotes compared to prokaryotes, their more rapid evolution compared to structured proteins, their organization into specific groups, their structural preferences, their half-lives in cells, their contributions to signaling diversity (via high contents of multiple-partner binding sites, post-translational modifications, and alternative splicing), their distinct functional repertoire compared to that of structured proteins, and their involvement in diseases.

DisProt: The database of disordered proteins

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 .

PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids

Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomic investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. Access to PONDR-FIT is available at www.disprot.org.

Length-dependent prediction of protein intrinsic disorder

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 http://www.ist.temple.edu/disprot/predictorVSL2.php

Classification of Intrinsically Disordered Regions and Proteins.

1.1. Uncharacterized Protein Segments Are a Source of Functional Novelty Over the past decade, we have observed a massive increase in the amount of information describing protein sequences from a variety of organisms.1,2 While this may reflect the diversity in sequence space, and possibly also in function space,3 a large proportion of the sequences lacks any useful function annotation.4,5 Often these sequences are annotated as putative or hypothetical proteins, and for the majority their functions still remain unknown.6,7 Suggestions about potential protein function, primarily molecular function, often come from computational analysis of their sequences. For instance, homology detection allows for the transfer of information from well-characterized protein segments to those with similar sequences that lack annotation of molecular function.8−10 Other aspects of function, such as the biological processes proteins participate in, may come from genetic- and disease-association studies, expression and interaction network data, and comparative genomics approaches that investigate genomic context.11−17 Characterization of unannotated and uncharacterized protein segments is expected to lead to the discovery of novel functions as well as provide important insights into existing biological processes. In addition, it is likely to shed new light on molecular mechanisms of diseases that are not yet fully understood. Thus, uncharacterized protein segments are likely to be a large source of functional novelty relevant for discovering new biology.

The protein trinity--linking function and disorder.

http://biotech.nature.com • SEPTEMBER 2001 • VOLUME 19 • nature biotechnology Interpreting function in terms of specific three-dimensional structure has dominated thinking about proteins for more than 100 years, starting with the lock-and-key proposal of Fischer1 and continuing with the equating of denaturation with loss of specific structure by Wu2 and independently at a slightly later date by Mirsky and Pauling3. This dependence of function on structure is even embedded in our language: unfolded protein and denatured protein are used interchangeably. Furthermore, the avalanche of protein three-dimensional structures determined by X-ray diffraction and by nuclear magnetic resonance (NMR)4 has diverted attention away from alternative views. Numerous counterexample proteins have surfaced over the years—proteins for which lack of three-dimensional structure is required for function. One clear example is calcineurin, a serine/threonine phosphatase that becomes activated by the binding of the Ca2+–calmodulin complex to a region that exists as a disordered ensemble5,6. The disorder spans the calmodulin binding site and is essential for calcineurin function. That is, when calmodulin binds to its target helix, the helix becomes completely surrounded7. Thus, the open, flexible disordered region of calcineurin provides the space needed by calmodulin so it can completely surround its target helix. Even though hundreds of other examples of proteins with intrinsic disorder have surfaced over the past 50 years, review articles on this topic are only just now beginning to appear8–10. Wright and Dyson8 suggested that the existence of proteins with intrinsic protein disorder calls for a reassessment of the protein–structure– function paradigm. Since amino acid sequence determines three-dimensional structure, amino acid sequence should also determine lack of three-dimensional structure. Furthermore, if intrinsic disorder provides the basis for some biological functions, then the operation of natural selection should conserve the lack of folding and thereby preserve those functions that depend on this property. If disorder is indeed encoded by the amino acid sequence, then predictors of disorder should exceed the accuracies expected by chance. Work in our group has used literature and database searches to collect a set of proteins structurally characterized to have regions of disorder, some of which were indicated by NMR to be wholly disordered under physiological conditions. Using this set of proteins with intrinsic disorder, we have set out to construct the predictors needed to test the hypothesis.