Multiparametric Analysis of Intrinsically Disordered Proteins: Looking

Dec 23, 2011 - Department of Molecular Medicine, University of South Florida, Tampa, ... For a more comprehensive list of citations to this article, u...
0 downloads 0 Views 3MB Size
Download PDF
Feature pubs.acs.org/ac

Multiparametric Analysis of Intrinsically Disordered Proteins: Looking at Intrinsic Disorder through Compound Eyes Intrinsically disordered proteins are highly abundant in various proteomes. They are different from ordered proteins at many levels, and their structural and functional characterization requires special experimental and computational tools. Vladimir N. Uversky*,†,‡ and A. Keith Dunker§ †

Department of Molecular Medicine, University of South Florida, Tampa, Florida 33612, United States Institute for Biological Instrumentation, Russian Academy of Sciences, 142292 Pushchino, Moscow Region, Russia § Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States ‡

S Supporting Information *

regions are highly abundant in any given proteome.4,7−10 Therefore, IDPs constitute a fourth tribe of the protein kingdom,11 a tribe to be added to the other three that include the following: (1) Fibrous proteins that are large insoluble supramolecular complexes, shaped like rods or wires and playing a structural or supportive role in the organism (e.g., fibrinogen, keratin, and collagen, see Figure 1A); (2) globular proteins that are predominantly found in aqueous environments of cytosol and extracellular fluids (see Figure 1B); and (3) integral transmembrane proteins that exist within the lipid environment of biological membranes (see Figure 1C). IDPs are different from “normal” ordered proteins at multiple levels. Amino acid sequence determines the ability of a protein to fold or not to fold under physiologic conditions.1,3,4,7,12,13 For example, in comparison with ordered proteins, IDPs are significantly depleted in order-promoting amino acids, such as Ile, Leu, Val, Trp, Tyr, Phe, Cys, and Asn and are substantially enriched in disorder-promoting amino acids, Ala, Arg, Gly, Gln, Ser, Glu, Lys, and Pro.4,14−17 Furthermore, several attributes of disordered segments, such as 14 Å contact number (defined as the number of Cα atoms located within a sphere of the radius of 14 Å surrounding a given residue and is derived from a statistical analysis of residues in proteins with known 3D structure), hydropathy, flexibility, β-sheet propensity, coordination number, compositions for groups of amino acids such as Arg + Glu + Ser + Pro and Cys + Phe + Tyr + Trp, side chain volume, and net charge all provide rather reliable discrimination between ordered and disordered proteins.4 Disordered regions share at least some common sequence features over many proteins.1,18 For example, the combination of low mean hydrophobicity (leading to low driving force for protein compaction) and high net charge (leading to strong electrostatic repulsion) represents an important prerequisite for the absence of compact structure in an intrinsically disordered protein.3 These features make IDPs identifiable and distinguishable from ordered proteins, and therefore such collections of sequence features can be used to design specific predictors of intrinsic disorder. Currently, the design of disorder predictors is a very active area of research, and as of 2009, more than 50 such tools had been developed .19 The existence of numerous computational tools that give prediction

■

WHAT ARE INTRINSICALLY DISORDERED PROTEINS? Many biologically active proteins do not have unique 3-D structures either along their entire lengths or in regions of various lengths, existing instead as highly dynamic conformational ensembles.1−7 These proteins and regions are described as intrinsically disordered proteins (IDPs) or IDP regions among other names. In contrast to the ordered or structured proteins, there are no fixed positions at equilibrium for the atoms and dihedral angles of IDPs. IDPs exist instead as highly dynamic ensembles whose atoms and backbone Ramachandran angles fluctuate significantly over time. IDP regions can be as short as a few amino acid residues, or such lack of fixed structure can be found through long disordered loops, ends, domains, or even throughout entire polypeptide chains. IDPs and IDP © 2011 American Chemical Society

Published: December 23, 2011 2096

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

of disorder far above that expected by chance provides direct support for the hypothesis that intrinsic disorder is encoded in a protein’s amino acid sequence. IDPs/IDP regions are different from ordered proteins at higher levels of structural organization too. IDPs possess characteristic and recognizable structural properties. For example, their conformational ensembles contain highly dynamic structures that interconvert on a number of time scales.7 Structurally, IDPs/IDP regions could be crudely grouped into two major classes, proteins with extended and compact disorder.6,7,12,13,20 According to this classification, IDPs can be less or more compact, possess smaller or larger amounts of flexible secondary structure and contain smaller or larger numbers of tertiary contacts. The extreme conformational plasticity of IDPs and IDP regions plays a crucial role in the intricate molecular mechanisms of their action. Functionally, IDPs complement ordered proteins and are commonly involved in a wide range of intermolecular interactions.21−23 IDP regions frequently contain sites of various posttranslational modifications.24,25 Although their highly dynamic nature seems to be perfectly suited to serve as a strong signal for rapid degradation of IDPs, a recent analysis revealed that protein half-lives in vivo show only very weak dependence on structural disorder.26 In fact, not all IDPs are subject to fast degradation, but all (or almost all) rapidly digested proteins are IDPs or contain IDP regions with signals for digestion. Many IDPs and IDP regions are known to undergo complete or partial disorder-toorder transition as a result of interaction with specific binding partner(s).27,28 Some IDP regions possess remarkable binding promiscuity, being able to interact specifically with structurally unrelated partners.29 Often, IDPs are associated with human diseases,30 and therefore these “protein-clouds” serve as very attractive drug targets, which are poorly exploited as of yet.31 All of these factors lead to a high level of interest in these proteins.

Figure 1. Representative members of the traditional protein classes: (A) fibrous proteins are extended supramolecular complexes that fold into rodor wire-like shapes. These are inert and often insoluble proteins that mainly serve structural or storage functions. Fibrous proteins have little or no tertiary structure, are organized as long parallel polypeptide chains and frequently contain numerous cross-linkages at intervals forming long fibres or sheets. Collagen (left) is a typical fibrous protein that makes up about 25−35% of the whole-body protein content (PDB ID, 1CAG). The collagen molecule is made of three polypeptide chains, each forms an elongated, left-handed helix (not as tightly wound as an α-helix), that wind around each other to form a three-stranded “rope”. Each chain contains ∼1000 amino acids and is mostly made of the repetitious amino acid sequence glycine−proline−hydroxyproline. Fibrinogen (right) contains three pairs of polypeptides: two Aα, two Bβ, and two γ (PDB ID, 1M1J), which are linked together by 29 disulfide bonds. Fibrinogen can undergo a remarkable transformation from soluble monomers (fibrinogen) to an insoluble polymer gel (polymerized fibrin). (B) Globular proteins are relatively spherical in shape as the name implies and are typically folded to enhance the protein solubility in water by placing polar groups at the protein surface (where they can participate in attractive interactions with water molecules) and by sequestering hydrophobic groups inside the molecule (where they are protected from interaction with water molecules and interact with other nonpolar groups). To saturate all the hydrogen bond donors and acceptors in the peptide backbone packed into a globular conformation, α-helices and the β-sheets are formed. The mostly β-structural proteins are exemplified by bovine β-lactoglobulin (left, PDB ID, 3UEU), whereas myoglobin (right) represent a typical example of an α-helical protein (PDB ID, 1MBN). (C) Integral membrane proteins: The β-barrel (left) is a closed, circular β-sheet composed of membrane spanning β-strands of 8−12 residues each. Known β-barrels have between 8 and 22 strands. This example is the 22-stranded outer membrane transporter FhuA from Escherichia coli (PDB ID, 1QJQ). The α-helical bundle (right) is exemplified by the E. coli ClC Cl−/H+ antiporter (PDB ID, 1KPK). Known α-helical membrane proteins have from 1 to a few dozen membranespanning helices. Individual helices are typically 18−26 residues long.

■

WHAT DO INTRINSICALLY DISORDERED PROTEINS DO? Functionally, IDPs are very different from ordered proteins too. Indeed, IDP functions complement those of ordered proteins7 and can be grouped into several broad functional classes (e.g., assemblers, chaperones, display sites, effectors, entropic chains, and scavengers5). IDPs are rarely responsible for catalysis by themselves, except for rare occasions in which collapsed IDPs exhibit catalytic activity.32−38 Also, a number of enzymes have been observed to contain IDP loops that fold over the top of substrates as they bind thereby helping to exclude water from the catalytic center.39−43 In contrast to their rare involvement in catalytic activity, IDPs are frequently responsible for noncatalytic binding, for example, binding to nucleic acids, metal ions, heme groups, other small molecules, proteins, and membrane bilayers.3,24 These various IDP-based binding interactions commonly lead to regulation, recognition, signaling, and control pathways, where high-specificity/low-affinity interactions with multiple partners are necessary prerequisites.7 Overall, IDPs can participate in a very diverse range of binding modes that produce a multitude of rather unusual complexes.44 Some of these complexes are relatively static and resemble complexes of ordered proteins. The structure of such static complexes can be determined by X-ray crystallography. In addition to the static complexes, some IDPs do not fold (partially or completely) even in their bound state, forming so-called disordered, dynamic, or fuzzy complexes. Such complexes are likely to be formed via multiple low affinity binding sites.44−48 2097

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

Figure 2. An IDP Identification Card: Major structural and functional features of intrinsically disordered proteins are listed.

ing and folding. These are so-called “entropic chain activities”, since they rely entirely on the constant motion of an extended random-coil-like polypeptide.4 The DisProt database (http:// www.disprot.org) provides structural and functional (where available) information on experimentally characterized IDPs. As of November 2011, the database contained information on 645 proteins (1388 disordered regions).54 It was pointed out that in cell signaling, intrinsically disordered regions are frequently used by proteins as a valid identification (ID) that is easily recognized by the other participants of the signaling cascade.23 In line with this idea, Figure 2 represents an IDP identification card which lists major structural and functional features of these remarkable members of the protein kingdom.

IDPs are promiscuous interactors that frequently serve as hubs (popular nodes) in protein interaction networks.22,23 Deletion of such heavily connected nodes is often lethal to the organism.49 In disordered hubs, IDP regions may be present in one of at least two forms:50 (1) a flexible linker that connects two ordered domains allowing their unrestricted movement with respect to each other and (2) a binding site that undergoes a disorder-to-order transition when associating with its partner. From another viewpoint, many IDPs are involved in the oneto-many and many-to-one binding.22,23 Typically the one-to-many interacting IDPs act as protein-chameleons that can adopt different conformations upon binding to different partners.29 For example, the intrinsically disordered C-terminal domain of p53 is involved in interaction with more than forty partners, but structures have been determined for just four of these complexes. Interestingly, these four complexes involve structurally unrelated partners. The p53 binding region adopts different secondary structure types upon binding with the unrelated partners, becoming an α-helix when bound to S100ββ, a β-strand when bound to sirtuin, and a coil with two distinct backbone trajectories when bound to CBP and cyclin A2.29 Furthermore, p53 utilizes different residues for the interactions with these four different partners. Therefore, intrinsic disorder allows IDPs to be “poly-linguistic” and communicate to different partners using different “languages”.29 There are several known examples in which many different IDPs with similar but not identical sequences compete for a single binding site on a structured protein,51,52 which we call many-to-one signaling.22 Illustrative examples of such multiple specificity ordered interactors are 14-3-3 proteins29 and calmodulin.53 The flexibility inherent in the disorder enables the slightly different sequences to adjust their structures upon binding to latch onto the same binding site of the structured partner.29 Although biological actions of IDPs frequently involve functional disorder-to-order transitions, some important activities ascribed to IDPs depend on the flexibility, pliability and plasticity of the backbone and do not directly involve coupled bind-

■

WHAT DO INTRINSICALLY DISORDERED PROTEINS REALLY LOOK LIKE? Intrinsically disordered regions (IDP regions) exist as dynamic ensembles, resembling “protein-clouds”.31 Although these protein clouds are highly dynamic, their structures can be described rather well by a rather limited number of conformations.55,56 These “protein-clouds” are quite different from ordered proteins shown in Figure 1. In fact, Figure 3 represents two illustrative examples of the intrinsically disordered conformational ensembles and shows that IDPs can resemble hairballs (Figure 3A), whereas IDP regions may possess strong resemblance to a person having a bad hair day (Figure 3B). Because of the highly dynamic nature of IDPs, structures do not converge to a single conformation resulting in the cloud-like appearance of the corresponding conformational ensembles the snap-shots of which contain an apparent multitude of the ends. Finally, Figure 3C represents the model structure of the nuclear pore complex (NPC), a large proteinaceous complex located within the nuclear envelope. NPC represents a large channel that connects the cytoplasm and nucleoplasm of eukaryotic 2098

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

■

Feature

HOW TO ANALYZE STRUCTURAL PROPERTIES AND DYNAMIC BEHAVIOR OF INTRINSICALLY DISORDERED PROTEINS? Examples shown in Figure 3 reflect the highly dynamic nature of an IDP structure and suggest that structural analysis of these proteins is not an easy task. In fact, the determination of a unique high-resolution structure is not possible for an isolated IDP, and rather complex methods have to be used to obtain experimental constraints on the ensemble of states that is sampled by the intrinsically disordered polypeptide chain. Therefore, IDPrelated structural studies typically rely on a host of biophysical methods that can provide information on the overall compactness of IDPs, their conformational stability, shape, residual secondary structure, transient long-range contacts, regions of restricted or enhanced mobility, etc. Because of their highly heterogeneous nature and conformational dynamics happening at multiple time-scales, the full spectrum of structural and dynamic characteristics of IDPs cannot be gained by a single tool and clearly requires a multiparametric approach. The use of a multiparametric approach for structural and dynamic characterization of IDPs gives a number of important advantages. In essence, multiparametric analysis resembles the compound eyes of insects, which compared with simple eyes possess a very large view angle and can detect fast movement.58 There is no need to go through the detailed analysis of different tools comprising the modern arsenal of biophysical techniques that can be used to characterize dynamic structure of IDPs since many of these methods have been the subjects of focused reviews and books.6,59−65 Many of these techniques were initially developed and then elaborated for the analysis of the structural properties and conformational behavior of ordered proteins and were not originally intended to provide information on protein-clouds without unique structure. To avoid potential misinterpretations, an extreme caution should be taken while interpreting data generated by these structure-centered approaches, since the information on the presence of intrinsic disorder is often the result of the absence of a signal characteristic for the ordered protein. Furthermore, simultaneous analysis of a given protein by several techniques sensitive to the different structural levels provides the most unambiguous characterization of its disordered ensemble. Some of the most popular techniques for the intrinsic disorder analysis are briefly outlined below. In X-ray crystallographic experiments, the increased flexibility of atoms in the disordered regions leads to the noncoherent Xray scattering and makes them “invisible”, therefore giving rise to regions with missing electron density. Indeed, the long history of the use of the term disorder by crystallographers in describing missing regions of electron density led the authors to choose “disorder” over other terms for these proteins.66,67 Although NMR spectroscopy is the technique of choice for providing high-resolution structural information on IDPs in solution,6,59−65 NMR methods have some technical limitations, such as protein size (increasing size leads to slower tumbling and to shorter spin−spin relaxation times and also leads to increasingly complex spectra), increased redundancy due to the presence of tandem repeats, lack of spectral dispersion due to similar environments for the various residues, and extreme line broadening due to the motional dynamics in the millisecond to microsecond time scale (which typically precludes direct NMR studies of molten globules). Also, NMR information arises from nuclei and their local environments, so NMR approaches provide

Figure 3. Structures of intrinsically disordered proteins and their “real life” analogues: (A) Conformational ensemble of the unbound HIV-1 transactivator of transcription protein Tat determined by NMR (1TIV). Representative members of the conformational ensemble are shown by ribbons of different colors. 118 This ensemble clearly resembles a hairball. (B) Conformational ensemble of the N-terminal domains of p53 within the full-length p53 molecule. Ordered part of the p53 comprising the core domain, linker, and tetramerization domains (gray) and DNA (magenta) are shown in space fill mode. The flexible C-terminal domain is not shown for reasons of clarity. N-terminal domains forming the four difference monomers are shown in different colors for clarity.119 Reproduced with the permission from the Proceedings of the National Academy of Sciences (Wells, M.; Tidow, H.; Rutherford, T. J.; Markwick, P.; Jensen, M. R.; Mylonas, E.; Svergun, D. I.; Blackledge, M.; Fersht, A. R. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 5762−5767, ref 119) Copyright 2008 National Academy of Sciences. This conformational ensemble of the N-terminal domains of p53 within the full-length p53 molecule resembles a person with a bad hair day. (C) Structure of the nuclear pore complex (http://sspatel. googlepages.com/nuclearporecomplex). Reproduced with the permission from All Media, Copyright 2001−2006, Samir S. Patel. Structurally,

cells and whose major function is to regulate the flow of proteins and RNA between these two compartments. The interior of this channel is filled with a flexible, dynamic mesh of nucleoporins containing long IDP regions, the constant thermal movements of which prevents large molecules from going through the channel. Therefore, structurally NPC resembles a bowl of noodles (see Figure 3C).57 The noodle-mesh-like inside of the NPC represents a very nice illustration of the “entropic chain activities” mentioned in the previous section. 2099

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

than molecular masses calculated from sequence data or measured by mass spectrometry.5,59,61 Detailed information on the hydrodynamic dimensions of a polypeptide provides very important constraints for the IDP structural characterization. IDPs have increased hydrodynamic volumes relative to the ordered proteins of similar molecular mass. The protein compaction degree can be evaluated by various techniques, such as gel-filtration, viscometry, smallangle X-ray scattering (SAXS), small angle neutron scattering (SANS), sedimentation, dynamic and static light scattering, etc. Analysis of the SAXS data in the form of a Kratky plot can provide crucial information on the degree of globularity of a given protein. Furthermore, SAXS can provide quantitative characterization of IDPs in solution via the utilization of the ensemble optimization method (EOM). Here, the flexibility of IDPs is taken into account by considering the coexistence of different conformations of the protein that are selected using a genetic algorithm from a pool containing a large number of randomly generated models covering the protein configurational space and that contribute to the experimental scattering pattern.72 A combination of SAXS with high-resolution techniques, such as NMR, can be used to generate reliable models and to gain unique structural insights about the IDP over multiple structural scales.73 Ion charge-state distribution (CSD) analysis in electrospray ionization mass spectrometry (ESI-MS) is a robust and fast technique for direct detection and characterization of coexisting protein conformations in solution. Here, compact folded proteins give rise to ESI-generated ions carrying a relatively small number of charges, whereas less compact conformers accommodate upon ESI a larger number of charges depending on the extent of their unfolding. ESI-MS allows direct detection and characterization of distinct conformers, including transiently populated ones, and transitions between them. This feature of ESI-MS is widely employed in the studies of protein dynamics,74−76 protein−ligand77 and protein−protein interactions,78 and shows a great potential for IDP structural characterization.79 Furthermore, the combination of gas-phase ion mobility spectrometry with mass spectrometry led to the development of ion mobility separation/spectroscopy−mass spectrometry (IMS-MS) that was shown to provide invaluable information on protein structure,80 including characterization of the effects of post-translational modifications on the IDP conformational ensemble.81 The techniques listed above provide structural description of the ensemble of molecules. Such techniques typically provide accurate description of a single ordered structure. Since IDPs represent a highly dynamic ensemble of interconverting conformations, their conformational landscape is characterized by the lack of global energy minimum and contains instead a set of shallow energy minima separated by low potential barriers. This landscape defines conformational behavior of IDPs, which is driven by the depths and profiles of their energy minima and by the effect of the environment upon them. Therefore, in addition to quantification of potential subpopulations, the accurate characterization of IDPs requires understanding of the properties of individual protein molecules. This can be achieved using various single-molecule techniques, e.g., single molecule fluorescence resonance energy transfer (SM-FRET)82 and atomic force microscopy-based single molecule force spectroscopy (SMFS).83 High-speed atomic force microscopy (AFM) can be used to visualize individual molecules in solution under physiological conditions.84

very little information regarding the overall size and shape of the IDPs (though overall size information is provided by diffusion estimates from line broadening, and atomistic ensemble description methods based on residual dipolar coupling (RDC) and paramagnetic relaxation enhancement (PRE) measurements coupled to ensemble optimization methods (EOM)62 can provide information on the maximal intramolecular distance and the volume of the representative conformational ensemble). Heteronuclear multidimensional NMR can be used for gaining precise structural information on IDPs/IDP regions via assignment of their resonances and can also provide direct measurement of the mobility of IDP regions. Heteronnuclear multidimensional NMR also provides information about the extent to which IDP regions (transiently) populate regular secondary structure elements. Although long-range contacts in IDPs are transient and difficult to detect by traditional NMR approaches (such as chemical shifts or long-range NOEs typically used for obtaining topological distance constraints in wellstructured proteins), paramagnetic relaxation enhancement (PRE) has been shown to be a highly successful tool for the unambiguous detection of the long-range contacts in disordered protein ensembles.60 In-cell NMR spectroscopy represents a very promising approach for structural characterization of IDPs in their natural environments (i.e., within cells). Successful in-cell characterization of IDPs has been reported for both bacterial and eukaryotic cells.68−71 However, the reliable execution of these experiments is extremely challenging and significant precaution should be taken while performing the in vivo NMR analysis to avoid generation of the misleading data. There is also a very wide range of so-called low-resolution spectroscopic techniques, which definitely can generate very useful information. Analysis of optical activity via circular dichroism, optical rotary dispersion, or Raman optical activity measurements can be used to evaluate protein tertiary and secondary structure. Fourier-transform infrared spectroscopy and deepUV resonance Raman spectroscopy report on the presence or absence of an ordered secondary structure too.59,63−65 Various fluorescence characteristics, such as shape and position of the intrinsic fluorescence spectrum, fluorescence anisotropy and lifetime, accessibility of the chromophore groups to external quenchers, steady state and time-resolved parameters of the fluorescent dyes, fluorescence resonance energy transfer, etc. can be used to describe the intramolecular mobility and compactness of an IDP. The increased flexibility of IDPs and IDP regions can be further validated by their increased susceptibility to limited proteolysis in vitro, whereas immunochemical methods may be successfully used to study the structural changes which a protein-immunogen undergoes during its function. Ordered (folded) and disordered (partially folded) proteins possess very different conformational stabilities, and therefore intrinsic disorder may be in principle detected by analyzing the peculiarities of the pH-, temperature-, and denaturant-induced conformational transitions. Useful information on conformational stability of IDPs and their dynamic peculiarities can be gained from the H/D exchange analysis. Because of their distinctive, significantly polar amino acid compositions, IDPs bind less sodium dodecyl sulfate (SDS) than ordered proteins. This results in their abnormal mobility in SDS polyacrylamide gel electrophoresis experiments, giving rise to the apparent molecular masses that are noticeably higher 2100

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

■

Feature

HOW CAN COMPUTATIONAL ANALYSIS HELP IN STRUCTURAL AND FUNCTIONAL CHARACTERIZATION OF IDPS? In addition to laboratory experiments, a key argument about the existence and distinctiveness of IDPs and IDP regions came from computational analysis. IDPs and IDP regions are noticeably different from ordered proteins and domains, and related sequence biases were exploited to develop a multitude of disorder predictors.19 Supplementary Table S1 in the Supporting Information represents the information related to several publicly available predictors of protein intrinsic disorder. More information about various computational tools developed for the evaluation of various intrinsic disorder aspects can be found in recent reviews.16,19,85−88 Since IDPs and IDP regions are reliably predictable from the sequence, disorder-oriented computational tools represent a very important addition to the analytical arsenal for the IDP characterization. Indeed, bioinformatics played a significant role in the development of the IDP field. Disorder predictions helped researchers to improve estimation of the commonness of the disorder and the functional repertoire of IDPs, facilitated their discovery, and helped with the characterization of important functional sites, such as binding regions and protein posttranslational modification sites. Disordered predictions also helped to provide insight into structural and dynamic properties of the proteins of interest, both in individual and highthroughput experiments.16,19,85 Since long disordered regions are generally not compatible with the crystallization process, disorder predictions can be used for delineating domains suitable for protein crystallization and structural genomics projects. Here, accounting for protein disorder can improve target selection and prioritization for the structural genomics projects. At the moment, computational tools represent the only reliable way for estimating the commonness of protein disorder in large data sets. They allow scientifically sound extrapolation of knowledge gained on the basis of a few examples to collections comprising hundreds or even thousands of proteins. For example, several computational studies indicated that IDPs are very common in various proteomes.4,7−10 Furthermore, proteins associated with cancer,21 cardio-vascular disease,89 and neurodegenerative diseases90 were shown to be enriched in intrinsic disorder. Intrinsic disorder was shown to be highly abundant in signaling proteins,21 transcription factors,91 PEST proteins,92 histones,93 serine/arginine-rich splicing factors,94 partners of 14-3-3 proteins,51 nucleoporins,95 and several other sets of proteins with different functions. Predictions of intrinsic disorder may provide very important clues for the analysis of individual proteins. Since the majority of disorder predictors are based on rather large training sets, the prediction of intrinsic disorder for a given protein provides a good basis for laboratory experiments. Furthermore, if a given protein is predicted to be disordered, it is statistically similar to IDPs used in the training of the predictors. This is a clear indication that the protein of interest is not an exception but a member of the well-defined tribe. Thus, using prediction to guide experiments is especially important for accelerating the characterization of IDPs and IDP regions,85 and disorder predictions might help in better understanding and interpreting experimental data. For example, a monomeric protein predicted to be intrinsically disordered is expected to possess a large hydrodynamic volume. However, if the protein was assumed to be globular, an experimentally observed large hydrodynamic dimension might be incorrectly interpreted in terms of oligomer

formation. Predictions of intrinsic disorder also play a vital role in classification of proteins into various structural groups. They can be of significant help for better understanding the protein function(s) too. In fact, the disorder predictions aided in structural characterization of a number of IDPs, such as retinal tetraspanin,96 nicotinic acetylcholine receptor,97 DBE,98 proapoptotic BH domain-containing family of proteins,99 transcriptional corepressor CtBP,100 Notch signaling pathway proteins,101 and many others. The use of bioinformatics tools based on intrinsic disorder phenomenon can be also used for protein functional analysis. Specialized computational approaches and data mining tools are recommended for searching large data sets for disorderrelated protein functions. For example, on the basis of the analysis of 200 000 functionally annotated Swiss-Prot proteins, it was concluded that out of 711 functional keywords associated with at least 20 proteins, 262 keywords were strongly positively correlated with predictions of long IDP regions, whereas 302 keywords were strongly negatively correlated with such regions. A significant fraction of these predictions were verified by comparing the inferred correlations to information found in the literature.102−104 Knowledge of the commonness of intrinsic disorder in various proteomes and information on the potential functional repertoire of IDPs represent an important foundation for subsequent laboratory experiments. Predictions of intrinsic disorder are important for finding sites of potential protein post-translational modifications, such as phosphorylation,25 methylation,105 and ubiquitination.106 Intrinsic disorder predictions can provide important hints for finding potential protein−protein and protein-nucleic acid interaction sites (molecular recognition features) and identify potential sites of post-translational modifications. For example, some peculiarities of the disorder prediction plots were shown to correspond to short, loosely structured binding regions that undergo disorder-to-order transitions upon binding to their partners.107 These regions were defined as molecular recognition features (MoRFs).28,108 On the basis of the structure adopted upon binding, at least three basic types of MoRFs were found: αMoRFs, β-MoRFs, and ι-MoRFs, which form α-helices, β-strands, and irregular secondary structure when bound, respectively.108 The large decrease in conformational entropy that accompanies disorder-to-order transitions uncouples specificity from binding strength. This phenomenon has the effect of making highly specific interactions easily reversible, which is beneficial for cells, especially in the inducible responses typically involved in signaling and regulation. A recent computational study of such binding illustrated that the disordered partner contains a “conformational preference” for the structure it will take upon binding and that these so-called “preformed elements” tend to be helices.109 Several MoRFs have been first noticed by prediction and later confirmed by experiment to be involved in protein− protein interactions.110−113 Another useful tool for finding potential binding sites is an ANCHOR algorithm, which relies on a pairwise energy estimation approach, which is a modification of the disorder prediction method used in the IUPred algorithm,114,115 and is based on the hypothesis that long regions of disorder contain localized potential binding sites that cannot form enough favorable intrachain interactions to fold on their own but are likely to gain stabilizing energy by interacting with a globular protein partner.116,117 The knowledge produced by tools for finding potential binding sites can be used to drive subsequent research with the major focuses in finding binding partners, 2101

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

analysis of resulting complexes, and searching for small molecules modulating these interactions.

Biophysics under the direction of Dr. Roland Rueckert. From 1969 to 1973, he carried out postdoctoral research at Yale University in the laboratory of Donald Marvin where he worked on the structure and cell penetration of the filamentous phage fd. Dr. Dunker started research in computational biology and bioinformatics in the mid-1980s and began using bioinformatics to study intrinsically disordered proteins in the mid-1990s, where he and his collaborators were the first to consider these proteins as a distinct class with important biological functions. His bioinformatics research goals include the improvement of intrinsic disorder predictions, especially with respect to identifying different types of disorder (flavors) and then to understanding the relationships between the different types of disorder and protein function. In addition, he combines bioinformatics prediction with laboratory experimentation to develop new approaches for understanding protein−protein and protein−nucleic acid signaling interactions that involve intrinsically disordered proteins. He can be contacted by e-mail at [email protected].

■

OUTLOOK The accurate characterization of the multifaceted phenomenon of intrinsic disorder clearly requires a multifaceted approach, ideally involving integrated use of both experimental and computational tools. Detailed biophysical studies utilizing a wide spectrum of techniques sensitive to the different levels of protein structure and to the dynamic behavior at different time scales are crucial for structural characterization of IDPs and for the clarification of the relationship between the highly dynamic structure of IDPs and their biological functions. Furthermore, various computational tools for finding and characterizing IDPs and IDP regions represent an important component of the multiparametric approach for analyzing these proteins. The computational tools provide crucial information about individual proteins, protein groups of various sizes, and even about entire proteomes.

■

■

ACKNOWLEDGMENTS This work was supported in part by Grant EF 0849803 from the National Science Foundation (to A.K.D and V.N.U.) and the Program of the Russian Academy of Sciences for “Molecular and Cellular Biology” (to V.N.U.).

ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

■

■

AUTHOR INFORMATION

REFERENCES

(1) Dunker, A. K.; Garner, E.; Guilliot, S.; Romero, P.; Albrecht, K.; Hart, J.; Obradovic, Z.; Kissinger, C.; Villafranca, J. E. Pac. Symp. Biocomput. 1998, 473−484. (2) Wright, P. E.; Dyson, H. J. J. Mol. Biol. 1999, 293, 321−331. (3) Uversky, V. N.; Gillespie, J. R.; Fink, A. L. Proteins 2000, 41, 415−427. (4) Dunker, A. K.; Lawson, J. D.; Brown, C. J.; Williams, R. M.; Romero, P.; Oh, J. S.; Oldfield, C. J.; Campen, A. M.; Ratliff, C. M.; Hipps, K. W.; Ausio, J.; Nissen, M. S.; Reeves, R.; Kang, C.; Kissinger, C. R.; Bailey, R. W.; Griswold, M. D.; Chiu, W.; Garner, E. C.; Obradovic, Z. J. Mol. Graph. Model. 2001, 19, 26−59. (5) Tompa, P. Trends Biochem. Sci. 2002, 27, 527−533. (6) Daughdrill, G. W.; Pielak, G. J.; Uversky, V. N.; Cortese, M. S.; Dunker, A. K. In Handbook of Protein Folding; Buchner, J., Kiefhaber, T., Eds.; Wiley-VCH, Verlag GmbH & Co. KGaA: Weinheim, Germany, 2005; pp 271−353. (7) Uversky, V. N.; Dunker, A. K. Biochim. Biophys. Acta 2010, 1804, 1231−1264. (8) Dunker, A. K.; Obradovic, Z.; Romero, P.; Garner, E. C.; Brown, C. J. Genome Inform. Ser. Workshop Genome Inform. 2000, 11, 161− 171. (9) Ward, J. J.; Sodhi, J. S.; McGuffin, L. J.; Buxton, B. F.; Jones, D. T. J. Mol. Biol. 2004, 337, 635−645. (10) Uversky, V. N. J. Biomed. Biotechnol. 2010, 2010, 568068. (11) Turoverov, K. K.; Kuznetsova, I. M.; Uversky, V. N. Prog. Biophys. Mol. Biol. 2010, 102, 73−84. (12) Uversky, V. N. Protein Sci. 2002, 11, 739−756. (13) Uversky, V. N. Eur. J. Biochem. 2002, 269, 2−12. (14) Williams, R. M.; Obradovi, Z.; Mathura, V.; Braun, W.; Garner, E. C.; Young, J.; Takayama, S.; Brown, C. J.; Dunker, A. K. Pac. Symp. Biocomput. 2001, 89−100. (15) Romero, P.; Obradovic, Z.; Li, X.; Garner, E. C.; Brown, C. J.; Dunker, A. K. Proteins 2001, 42, 38−48. (16) Radivojac, P.; Iakoucheva, L. M.; Oldfield, C. J.; Obradovic, Z.; Uversky, V. N.; Dunker, A. K. Biophys. J. 2007, 92, 1439−1456. (17) Vacic, V.; Uversky, V. N.; Dunker, A. K.; Lonardi, S. BMC Bioinf. 2007, 8, 211. (18) Garner, E.; Cannon, P.; Romero, P.; Obradovic, Z.; Dunker, A. K. Genome Inform. Ser. Workshop Genome Inform. 1998, 9, 201−213. (19) He, B.; Wang, K.; Liu, Y.; Xue, B.; Uversky, V. N.; Dunker, A. K. Cell Res. 2009, 19, 929−949.

Corresponding Author

*Address: Department of Molecular Medicine, University of South Florida, College of Medicine, 12901 Bruce B. Downs Blvd., MDC07, Tampa, Florida 33612, United States. Phone: 1-813-974-5816. Fax: 1-813-974-7357. E-mail: vuversky@ health.usf.edu.

BIOGRAPHY Vladimir N. Uversky is an Associate Professor at the Department of Molecular Medicine at the University of South Florida (Tampa, Florida) and a Leading Scientist at the Institute for Biological Instrumentation, Russian Academy of Sciences (Pushchino, Moscow Region, Russia). He obtained his academic degrees from Moscow Institute of Physics and Technology (Ph.D., in 1991) and from the Institute of Experimental and Theoretical Biophysics, Russian Academy of Sciences (D.Sc., in 1998). He spent his early career working mostly on protein folding at the Institute of Protein Research and the Institute for Biological Instrumentation (Russia). In 1998, he moved to the University of California Santa Cruz where for 6 years he was studying protein folding, misfolding, protein conformation diseases, and protein intrinsic disorder phenomenon. In 2004, he was invited to join the Indiana University School of Medicine as a Senior Research Professor to work on the intrinsically disordered proteins. Since 2010, Professor Uversky is with the University of South Florida, where he continues to study intrinsically disordered proteins and protein folding and misfolding processes. He has authored over 400 scientific publications and edited several books and book series on protein structure, function, folding, and misfolding. Contact Vladimir Uversky at the University of South Florida, Tampa, FL 33612; e-mail, [email protected]. A. Keith Dunker is the Director of the Center for Computational Biology & Bioinformatics and a T.-K. Li Professor of Medical Research at the Department of Biochemistry and Molecular Biology of the Indiana University School of Medicine (Indianapolis, Indiana). After receiving his B.S. in Chemistry from UC Berkeley in 1965, Dr. Dunker attended the University of Wisconsin at Madison where he earned his M.S. in Physics and his Ph.D. in 2102

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

(20) Dunker, A. K.; Obradovic, Z. Nat. Biotechnol. 2001, 19, 805− 806. (21) Iakoucheva, L. M.; Brown, C. J.; Lawson, J. D.; Obradovic, Z.; Dunker, A. K. J. Mol. Biol. 2002, 323, 573−584. (22) Dunker, A. K.; Cortese, M. S.; Romero, P.; Iakoucheva, L. M.; Uversky, V. N. FEBS J. 2005, 272, 5129−5148. (23) Uversky, V. N.; Oldfield, C. J.; Dunker, A. K. J. Mol. Recognit. 2005, 18, 343−384. (24) Dunker, A. K.; Brown, C. J.; Lawson, J. D.; Iakoucheva, L. M.; Obradovic, Z. Biochemistry 2002, 41, 6573−6582. (25) Iakoucheva, L. M.; Radivojac, P.; Brown, C. J.; O’Connor, T. R.; Sikes, J. G.; Obradovic, Z.; Dunker, A. K. Nucleic Acids Res. 2004, 32, 1037−1049. (26) Tompa, P.; Prilusky, J.; Silman, I.; Sussman, J. L. Proteins 2008, 71, 903−909. (27) Dyson, H. J.; Wright, P. E. Curr. Opin. Struct. Biol. 2002, 12, 54−60. (28) Oldfield, C. J.; Cheng, Y.; Cortese, M. S.; Romero, P.; Uversky, V. N.; Dunker, A. K. Biochemistry 2005, 44, 12454−12470. (29) Oldfield, C. J.; Meng, J.; Yang, J. Y.; Yang, M. Q.; Uversky, V. N.; Dunker, A. K. BMC Genomics 2008, 9 (Suppl 1), S1. (30) Uversky, V. N.; Oldfield, C. J.; Dunker, A. K. Annu. Rev. Biophys. 2008, 37, 215−246. (31) Dunker, A. K.; Uversky, V. N. Curr. Opin. Pharmacol. 2010, 10, 782−788. (32) Uversky, V. N.; Kutyshenko, V. P.; Protasova, N.; Rogov, V. V.; Vassilenko, K. S.; Gudkov, A. T. Protein Sci. 1996, 5, 1844−1851. (33) Pervushin, K.; Vamvaca, K.; Vogeli, B.; Hilvert, D. Nat. Struct. Mol. Biol. 2007, 14, 1202−1206. (34) Vamvaca, K.; Jelesarov, I.; Hilvert, D. J. Mol. Biol. 2008, 382, 971−977. (35) Woycechowsky, K. J.; Choutko, A.; Vamvaca, K.; Hilvert, D. Biochemistry 2008, 47, 13489−13496. (36) Zambelli, B.; Cremades, N.; Neyroz, P.; Turano, P.; Uversky, V. N.; Ciurli, S. Mol. Biosyst. 2012, 8, 220−228. (37) Neyroz, P.; Zambelli, B.; Ciurli, S. Biochemistry 2006, 45, 8918− 8930. (38) Zambelli, B.; Stola, M.; Musiani, F.; De Vriendt, K.; Samyn, B.; Devreese, B.; Van Beeumen, J.; Turano, P.; Dikiy, A.; Bryant, D. A.; Ciurli, S. J. Biol. Chem. 2005, 280, 4684−4695. (39) Fetrow, J. S. FASEB J. 1995, 9, 708−717. (40) Wang, G. P.; Cahill, S. M.; Liu, X.; Girvin, M. E.; Grubmeyer, C. Biochemistry 1999, 38, 284−295. (41) Vandemeulebroucke, A.; De Vos, S.; Van Holsbeke, E.; Steyaert, J.; Versees, W. J. Biol. Chem. 2008, 283, 22272−22282. (42) Malabanan, M. M.; Amyes, T. L.; Richard, J. P. Curr. Opin. Struct. Biol. 2010, 20, 702−710. (43) Yuzenkova, Y.; Zenkin, N. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 10878−10883. (44) Uversky, V. N. Chem. Soc. Rev. 2011, 40, 1623−1634. (45) Nash, P.; Tang, X.; Orlicky, S.; Chen, Q.; Gertler, F. B.; Mendenhall, M. D.; Sicheri, F.; Pawson, T.; Tyers, M. Nature 2001, 414, 514−521. (46) Mittag, T.; Orlicky, S.; Choy, W. Y.; Tang, X.; Lin, H.; Sicheri, F.; Kay, L. E.; Tyers, M.; Forman-Kay, J. D. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 17772−17777. (47) Mittag, T.; Marsh, J.; Grishaev, A.; Orlicky, S.; Lin, H.; Sicheri, F.; Tyers, M.; Forman-Kay, J. D. Structure 2010, 18, 494−506. (48) Tompa, P.; Fuxreiter, M. Fuzzy complexes: polymorphism and structural disorder in protein−protein interactions. Trends Biochem Sci. 2008 Jan;33(1):2−8. Epub 2007 Nov 28. PubMed PMID: 18054235. (49) Jeong, H.; Mason, S. P.; Barabasi, A. L.; Oltvai, Z. N. Nature 2001, 411, 41−42. (50) Patil, A.; Kinoshita, K.; Nakamura, H. Int. J. Mol. Sci. 2010, 11, 1930−1943. (51) Bustos, D. M.; Iglesias, A. A. Proteins 2006, 63, 35−42. (52) Bustos, D. M. Mol. Biosyst. 2012, 8, 178−184. (53) Radivojac, P.; Vucetic, S.; O’Connor, T. R.; Uversky, V. N.; Obradovic, Z.; Dunker, A. K. Proteins 2006, 63, 398−410.

(54) Sickmeier, M.; Hamilton, J. A.; LeGall, T.; Vacic, V.; Cortese, M. S.; Tantos, A.; Szabo, B.; Tompa, P.; Chen, J.; Uversky, V. N.; Obradovic, Z.; Dunker, A. K. Nucleic Acids Res. 2007, 35, D786−793. (55) Choy, W. Y.; Forman-Kay, J. D. J. Mol. Biol. 2001, 308, 1011− 1032. (56) Huang, A.; Stultz, C. M. PLoS Comput. Biol. 2008, 4, e1000155. (57) Yamada, J.; Phillips, J. L.; Patel, S.; Goldfien, G.; CalestagneMorelli, A.; Huang, H.; Reza, R.; Acheson, J.; Krishnan, V. V.; Newsam, S.; Gopinathan, A.; Lau, E. Y.; Colvin, M. E.; Uversky, V. N.; Rexach, M. F. Mol. Cell. Proteomics 2010, 9, 2205−2224. (58) Völkel, R.; Eisner, M.; Weible, K. J. Microelectron. Eng. 2003, 67−68, 461−472. (59) Receveur-Brechot, V.; Bourhis, J. M.; Uversky, V. N.; Canard, B.; Longhi, S. Proteins 2006, 62, 24−45. (60) Eliezer, D. Curr. Opin. Struct. Biol. 2009, 19, 23−30. (61) Iakoucheva, L. M.; Kimzey, A. L.; Masselon, C. D.; Smith, R. D.; Dunker, A. K.; Ackerman, E. J. Aberrant mobility phenomena of the DNA repair protein XPA. Protein Sci. 2001 Jul;10(7):1353-62. PubMed PMID: 11420437; PubMed Central PMCID: PMC2374115. (62) Jensen, M. R.; Salmon, L.; Nodet, G.; Blackledge, M. J. Am. Chem. Soc. 2010, 132, 1270−1272. (63) Longhi, S., Uversky, V. N., Eds. Instrumental Analysis of Intrinsically Disordered Proteins: Assessing Structure and Conformation; John Wiley & Sons, Inc.: Hoboken, NJ, 2010. (64) Uversky, V. N., Dunker, A. K., Eds. Experimental Tools for the Analysis of Intrinsically Disordered Protein, Vol. II; Humana Press: Totowa, NJ, 2012. (65) Uversky, V. N., Dunker, A. K., Eds. Experimental Tools for the Analysis of Intrinsically Disordered Protein: Vol. I; Humana Press: Totowa, NJ, 2012. (66) Arnone, A.; Bier, C. J.; Cotton, F. A.; Day, V. W.; Hazen, E. E. Jr.; Richardson, D. C.; Yonath, A.; Richardson, J. S. J. Biol. Chem. 1971, 246, 2302−2316. (67) Le Gall, T.; Romero, P. R.; Cortese, M. S.; Uversky, V. N.; Dunker, A. K. J. Biomol. Struct. Dyn. 2007, 24, 325−342. (68) Dedmon, M. M.; Patel, C. N.; Young, G. B.; Pielak, G. J. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 12681−12684. (69) Li, C.; Charlton, L. M.; Lakkavaram, A.; Seagle, C.; Wang, G.; Young, G. B.; Macdonald, J. M.; Pielak, G. J. J. Am. Chem. Soc. 2008, 130, 6310−6311. (70) McNulty, B. C.; Young, G. B.; Pielak, G. J. J. Mol. Biol. 2006, 355, 893−897. (71) Bodart, J. F.; Wieruszeski, J. M.; Amniai, L.; Leroy, A.; Landrieu, I.; Rousseau-Lescuyer, A.; Vilain, J. P.; Lippens, G. J. Magn. Reson. 2008, 192, 252−257. (72) Bernado, P.; Mylonas, E.; Petoukhov, M. V.; Blackledge, M.; Svergun, D. I. J. Am. Chem. Soc. 2007, 129, 5656−5664. (73) Receveur-Brechot, V.; Durand, D. Curr. Protein Pept. Sci. 2012, PMID: 22044150. (74) Konermann, L.; Douglas, D. J. Rapid Commun. Mass Spectrom. 1998, 12, 435−442. (75) Frimpong, A. K.; Abzalimov, R. R.; Eyles, S. J.; Kaltashov, I. A. Anal. Chem. 2007, 79, 4154−4161. (76) Invernizzi, G.; Grandori, R. Rapid Commun. Mass Spectrom. 2007, 21, 1049−1052. (77) Zhang, M.; Gumerov, D. R.; Kaltashov, I. A.; Mason, A. B. J. Am. Soc. Mass Spectrom. 2004, 15, 1658−1664. (78) Griffith, W. P.; Kaltashov, I. A. Biochemistry 2003, 42, 10024− 10033. (79) Frimpong, A. K.; Abzalimov, R. R.; Uversky, V. N.; Kaltashov, I. A. Proteins 2010, 78, 714−722. (80) Kanu, A. B.; Dwivedi, P.; Tam, M.; Matz, L.; Hill, H. H. Jr. J. Mass Spectrom. 2008, 43, 1−22. (81) Maurizio, E.; Cravello, L.; Brady, L.; Spolaore, B.; Arnoldo, L.; Giancotti, V.; Manfioletti, G.; Sgarra, R. J. Proteome Res. 2011, 10, 3283−3291. (82) Trexler, A. J.; Rhoades, E. Biochemistry 2009, 48, 2304−2306. 2103

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104

Analytical Chemistry

Feature

(83) Sandal, M.; Valle, F.; Tessari, I.; Mammi, S.; Bergantino, E.; Musiani, F.; Brucale, M.; Bubacco, L.; Samori, B. PLoS Biol. 2008, 6, e6. (84) Miyagi, A.; Tsunaka, Y.; Uchihashi, T.; Mayanagi, K.; Hirose, S.; Morikawa, K.; Ando, T. ChemPhysChem 2008, 9, 1859−1866. (85) Bracken, C.; Iakoucheva, L. M.; Romero, P. R.; Dunker, A. K. Curr. Opin. Struct. Biol. 2004, 14, 570−576. (86) Longhi, S.; Lieutaud, P.; Canard, B. Methods Mol. Biol. 2010, 609, 307−325. (87) Ferron, F.; Longhi, S.; Canard, B.; Karlin, D. Proteins 2006, 65, 1−14. (88) Bourhis, J. M.; Canard, B.; Longhi, S. Curr. Protein Pept. Sci. 2007, 8, 135−149. (89) Cheng, Y.; LeGall, T.; Oldfield, C. J.; Dunker, A. K.; Uversky, V. N. Biochemistry 2006, 45, 10448−10460. (90) Uversky, V. N. Front. Biosci. 2009, 14, 5188−5238. (91) Liu, J.; Perumal, N. B.; Oldfield, C. J.; Su, E. W.; Uversky, V. N.; Dunker, A. K. Biochemistry 2006, 45, 6873−6888. (92) Singh, G. P.; Ganapathi, M.; Sandhu, K. S.; Dash, D. Proteins 2006, 62, 309−315. (93) Hansen, J. C.; Lu, X.; Ross, E. D.; Woody, R. W. J. Biol. Chem. 2006, 281, 1853−1856. (94) Haynes, C.; Iakoucheva, L. M. Nucleic Acids Res. 2006, 34, 305− 312. (95) Denning, D. P.; Patel, S. S.; Uversky, V.; Fink, A. L.; Rexach, M. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 2450−2455. (96) Ritter, L. M.; Arakawa, T.; Goldberg, A. F. Protein Pept. Lett. 2005, 12, 677−686. (97) Kukhtina, V.; Kottwitz, D.; Strauss, H.; Heise, B.; Chebotareva, N.; Tsetlin, V.; Hucho, F. J. Neurochem. 2006, 97, Supp. 1, 63−67. (98) Yiu, C. P.; Beavil, R. L.; Chan, H. Y. Biochem. Biophys. Res. Commun. 2006, 343, 311−318. (99) Hinds, M. G.; Smits, C.; Fredericks-Short, R.; Risk, J. M.; Bailey, M.; Huang, D. C.; Day, C. L. Cell Death Differ. 2007, 14, 128−136. (100) Nardini, M.; Svergun, D.; Konarev, P. V.; Spano, S.; Fasano, M.; Bracco, C.; Pesce, A.; Donadini, A.; Cericola, C.; Secundo, F.; Luini, A.; Corda, D.; Bolognesi, M. Protein Sci. 2006, 15, 1042−1050. (101) Popovic, M.; Coglievina, M.; Guarnaccia, C.; Verdone, G.; Esposito, G.; Pintar, A.; Pongor, S. Protein Expr. Purif. 2006, 47, 398− 404. (102) Vucetic, S.; Xie, H.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V. N. J. Proteome Res. 2007, 6, 1899− 1916. (103) Xie, H.; Vucetic, S.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V. N. J. Proteome Res. 2007, 6, 1917− 1932. (104) Xie, H.; Vucetic, S.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Uversky, V. N.; Obradovic, Z. J. Proteome Res. 2007, 6, 1882− 1898. (105) Daily, K. M.; Radivojac, P.; Dunker, A. K. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB, San Diego, CA, November 2005; pp 475−481. (106) Radivojac, P.; Vacic, V.; Haynes, C.; Cocklin, R. R.; Mohan, A.; Heyen, J. W.; Goebl, M. G.; Iakoucheva, L. M. Proteins 2010, 78, 365− 380. (107) Garner, E.; Romero, P.; Dunker, A. K.; Brown, C.; Obradovic, Z. Genome Inf. 1999, 10, 41−50. (108) Mohan, A.; Oldfield, C. J.; Radivojac, P.; Vacic, V.; Cortese, M. S.; Dunker, A. K.; Uversky, V. N. J. Mol. Biol. 2006, 362, 1043−1059. (109) Fuxreiter, M.; Simon, I.; Friedrich, P.; Tompa, P. J. Mol. Biol. 2004, 338, 1015−1026. (110) Johansson, K.; Bourhis, J. M.; Campanacci, V.; Cambillau, C.; Canard, B.; Longhi, S. J. Biol. Chem. 2003, 278, 44567−44573. (111) Longhi, S.; Receveur-Brechot, V.; Karlin, D.; Johansson, K.; Darbon, H.; Bhella, D.; Yeo, R.; Finet, S.; Canard, B. J. Biol. Chem. 2003, 278, 18638−18648. (112) Callaghan, A. J.; Aurikko, J. P.; Ilag, L. L.; Gunter Grossmann, J.; Chandran, V.; Kuhnel, K.; Poljak, L.; Carpousis, A. J.; Robinson, C. V.; Symmons, M. F.; Luisi, B. F. J. Mol. Biol. 2004, 340, 965−979.

(113) Bourhis, J. M.; Johansson, K.; Receveur-Brechot, V.; Oldfield, C. J.; Dunker, K. A.; Canard, B.; Longhi, S. Virus Res. 2004, 99, 157− 167. (114) Dosztanyi, Z.; Csizmok, V.; Tompa, P.; Simon, I. J. Mol. Biol. 2005, 347, 827−839. (115) Dosztanyi, Z.; Csizmok, V.; Tompa, P.; Simon, I. Bioinformatics 2005, 21, 3433−3434. (116) Dosztanyi, Z.; Meszaros, B.; Simon, I. Bioinformatics 2009, 25, 2745−2746. (117) Meszaros, B.; Simon, I.; Dosztanyi, Z. PLoS Comput. Biol. 2009, 5, e1000376. (118) Shojania, S.; O’Neil, J. D. J. Biol. Chem. 2006, 281, 8347−8356. (119) Wells, M.; Tidow, H.; Rutherford, T. J.; Markwick, P.; Jensen, M. R.; Mylonas, E.; Svergun, D. I.; Blackledge, M.; Fersht, A. R. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 5762−5767.

2104

dx.doi.org/10.1021/ac203096k | Anal. Chem. 2012, 84, 2096−2104