Báo cáo khoa học: Flexible nets The roles of intrinsic disorder in protein interaction networks potx

MINIREVIEW
Flexible nets
The roles of intrinsic disorder in protein interaction networks
A. Keith Dunker
1
, Marc S. Cortese
1
, Pedro Romero
1,2
, Lilia M. Iakoucheva
3
and
Vladimir N. Uversky
1,4
1 Department of Biochemistry and Molecular Biology, and the Center for Computational Biology and Bioinformatics, Indiana University
School of Medicine, Indianapolis, IN, USA
2 School of Informatics, Indiana University – Purdue University Indianapolis, IN, USA
3 Laboratory of Statistical Genetics, The Rockefeller University, New York, NY, USA
4 Institute for Biological Instrumentation, Russian Academy of Sciences, Moscow Region, Russia
Scale-free networks and hubs
In biological systems, processes such as growth, energy
generation, cell division and signaling are integrated by
large, intricate networks. These biological networks, as
well as certain nonbiological networks, especially those
involved in communications such as the internet and
cellular phone systems, are classified as scale-free net-
works (SFNs) [1–3]. The basic feature that separates
these networks from non-SFNs such as regular
Correspondence
A.K. Dunker, Department of Biochemistry
and Molecular Biology, and the Center for

Computational Biology and Bioinformatics,
Indiana University School of Medicine,
714 N Senate Ave, Suite 250, Indianapolis,
IN 46202, USA
E-mail:
(Received 25 May 2005, revised 23 August
2005, accepted 30 August 2005)
doi:10.1111/j.1742-4658.2005.04948.x
Proteins participate in complex sets of interactions that represent the mech-
anistic 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 possi-
bility 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 dis-
order 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, intrin-
sically 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.
Abbreviations

CaM, calmodulin; Cdk, cyclin-dependent protein kinase; CKI, Cdk inhibitor protein; GSK3b, glycogen synthase kinase 3 beta; ID, intrinsically
disordered; MoRE, molecular recognition element; NER, nucleotide excision repair; PDB, Protein Data Bank; PONDRÒ, predictors of naturally
disordered regions; RGN, regular network; RNN, random network; SFN, scale-free network; XPA, xeroderma pigmentosum group A protein;
FRAT, frequently rearranged in advanced T-cell lymphomas; Wnt, wingless type MMTV integration site family; HMG, high mobility group;
VL-XT, a predictor of intrinsic disorder that integrates various methods-based predictor of long disordered regions and X-ray based N-andC-
terminal predictors; VSL1, length-dependent predictor of intrinsic protein disorder; RPA, replication protein A; ERCC1, exchange repair cross
complementing complex 1; TFIIH, transcription factor IIH; XAB, XPA binding protein; p27
Kip
, cyclin-dependent kinase inhibitor protein p27/1B.
FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5129
networks (RGNs) or random networks (RNNs) is the
presence of hubs. Hubs are highly connected nodes
that have hundreds, thousands or even millions of
links [1,4]. The existence of hubs and their frequency
impart two features to SFNs that provide substantial
benefit to large complex networks: (a) increased
robustness with regard to random defects and (b) shor-
ter distances (in terms of the number of intervening
nodes) between any two points [5]. RGNs are grid-like
with invariant node connectivity, whereas RNNs are
characterized by stochastic variations in node connec-
tivity [5,6]. Despite the random placement of links in
RNNs, the vast majority of nodes still have approxi-
mately the same connectivity.
Figure 1A,B compare an RNN to a similar-sized
SFN to illustrate an important property of the latter.
In SNFs, the hub nodes are connected to a dramatic-
ally greater fraction of all nodes than the nodes with
high connectivity in RNNs [7]. This provides the abil-
ity for a signal to travel from any node to any other

by traversing a small number of intervening nodes.
This feature imparts the so called small world property
to SFNs [4,8]. Extending the example to a biological
context, Fig. 1C represents an experimentally derived
Saccharomyces cerevisiae protein–protein interaction
map with 1870 protein ‘nodes’ connected by 2240
direct physical interactions [9]. Visual comparison of
the model SFN (Fig. 1B) with the experimentally
C
A
B
Fig. 1. Comparison of simulated and actual
protein interaction networks. (A) The ran-
dom network is rather homogeneous as
most nodes have approximately the same
number of links. (Reproduced from [205]
with the permission of the author, ª Insti-
tute of Physics and IOP Publishing Ltd.,
2000–05.) (B) A scale-free network (SFN) is
extremely inhomogeneous; while the major-
ity of nodes have one or two links, a few
nodes have a large number of links. To
show this, five nodes with the highest num-
ber of links are colored red, and their first
neighbors are colored green. While in the
random network only 27% of the nodes are
reached by the five most connected nodes,
in the SFN more than 60% are. This demon-
strates the key role that hubs play in the
SFN. Note that both networks contain the

same number of nodes (130) and links
(430). (Reproduced from [205] with the per-
mission of the author, ª Institute of Physics
and IOP Publishing Ltd., 2000–05.) (C) Yeast
protein interaction network map. The color
of a node signifies the phenotypic effect of
removing the corresponding protein (red,
lethal; green, nonlethal; orange, slow
growth; yellow, unknown). (Modified from
[9] with the permission of the authors,
ª Nature Publishing Group, 1998–2005).
Flexible nets A. K. Dunker et al.
5130 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
derived protein–protein interaction network (Fig. 1C)
suggests a similar underlying architecture.
The scale-free nature of protein–protein interaction
networks gives them the advantages of high connectiv-
ity and robustness. For example, the connectedness of
RNNs decays steadily as nodes fail in a random fash-
ion. The surviving network breaks into progressively
smaller and increasingly separate subnets that lose the
ability to communicate with one another. On the other
hand, SFNs show much less degradation from random
node failure because highly connected hubs serve to
maintain the integrity of the network. Because SFN
hubs comprise a small fraction of total nodes, they are
statistically less likely to fail as a result of random
deleterious events. Although the error tolerance of
SFNs is high, it is important to note that the failure of
hubs quickly leads to the breakdown of connectivity

[7]. This suggests that the SFNs are particularly resist-
ant to random node removal but are extremely sensi-
tive to targeted hub removal. In agreement with these
observations, analysis of the S. cerevisiae protein–pro-
tein interaction network revealed that although pro-
teins with five or fewer links constitute about 93% of
the total number of proteins, only about 21% of them
are essential to cell survival. By contrast, only 0.7% of
yeast proteins are known to have more than 15 links
(i.e., hubs), but for 62% of these, deletion is lethal [9].
A few caveats about the scale-free nature of biologi-
cal networks are in order. First, the network coverage
of S. cerevisiae [10,11], Caenorhabditis elegans [12], and
Drosophila melanogaster [13] interactomes elucidated to
date is sufficient only to suggest scale-free architecture.
That is, the examined networks appear to be scale-free
in nature, but the data constitute only a small fraction
of the full interactomes. That the scale-free nature of
biological protein–protein interaction networks is cur-
rently only a prediction was demonstrated by Han
et al. [14]. These collaborators constructed simulated
networks with random, exponential, scale-free and
truncated normal topologies and a range of average
connectivities. When the four topologies were sampled
at the level comparable to that used to construct cur-
rent protein–protein interaction maps, there was insuf-
ficient distinction among the derived sample networks
to unequivocally assign the underlying architectures.
Thus, conclusive proof that biological protein–protein
interaction networks maintain a scale-free nature

throughout full interactomes remains to be verified.
Secondly, experimental protein–protein interaction
data contains a significant fraction of false negatives
and positives. This has been illustrated by studies com-
paring the results from various large-scale efforts [15–
18]. Increasing the quality of existing data can be
addressed by comparing and combining datasets and
adding additional methods of analysis such as gene
neighborhood, gene co-occurrence, gene fusion events,
mRNA expression correlations and lethality of knock-
outs [19]. Despite the above described limitations, ana-
lysis of existing protein–protein interaction data can
lead to useful information.
Comparison of protein–protein interaction networks
across species has significant potential for the study of
molecular evolution. Useful tools have been construc-
ted for the comparison of protein–protein interaction
networks across species [20–23], and interesting evolu-
tionary inferences are being made, such as the observa-
tion that proteins having a more central position in
the networks of different species (i.e., hubs) appear to
evolve more slowly and are evidently more likely to be
essential for survival. These observations are consistent
with Fisher’s classic proposal that pleiotropy con-
strains evolution [24,25]. Other important considera-
tions include the timing and the locations of the hub
protein interactions. Some hub proteins have multiple
simultaneous interactions (party hubs), while others
have multiple sequential interactions separated in time
or in space (date hubs) [26]. It has been suggested that

date hubs organize the proteome, connecting biological
processes (modules; [27]) to each other, whereas party
hubs act inside functional modules [26] and thereby
may form scaffolds for various molecular machines or
coordinated processes.
Neither the classical lock-and-key [28] nor the ori-
ginal induced fit mechanism [29] readily accommodates
the multiple interactions of hub proteins, especially at
higher connectivities. Therefore, the existence of highly
connected hubs in scale-free protein–protein interaction
networks suggests a different mechanism for molecular
recognition. In fact, highly connected proteins were
suggested to have some special, perhaps even common,
structural features that would endow them with the
ability to carry out highly specific interactions with
many different proteins [30]. Gunasekaran et al. postu-
lated that the relatively large solvent-accessible surface
areas of extended disordered proteins could provide the
potential for large intermolecular surfaces with less
impact on cell size than if the same surfaces were provi-
ded by structured proteins [31]. Rather than maintain-
ing a smaller cell size, the key advantage of intrinsic
disorder may lie in providing the molecular basis for
the existence, flexibility, and evolution of interaction
networks. The following section explores a novel pro-
tein-disorder based mechanism that could provide the
structural features necessary to allow proteins to carry
out highly specific interactions with multiple, structur-
ally diverse partners.
A. K. Dunker et al. Flexible nets

FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5131
Many proteins have been shown to exist under
apparently physiological conditions as dynamic ensem-
bles. Instead of having relatively fixed bonds and
angles as in structured proteins, the backbone bonds
and angles of such proteins vary significantly over
time, with no specific equilibrium values while under-
going noncooperative conformational changes. In
other words, such proteins or protein regions do not
have rigid 3D structure under physiological conditions
in vitro [31–47]. Furthermore, these intrinsically disor-
dered (ID) proteins and regions are known to carry
out numerous biological functions including cell signa-
ling [35], molecular recognition [48], and various
other interactions with proteins and nucleic acids
[32,34,35,37,39–43,49–51].
Recently, a number of groups have published predic-
tors of protein disorder, several of which are available
on the web (reviewed in [48]; see also http://www.
disprot.org). These predictors are based on the
assumption that the absence of rigid structure is enco-
ded in specific features of the amino acid sequence
[52,53]. In fact, statistical analysis shows that amino
acid sequences encoding for ID proteins or regions are
significantly different from those of ordered proteins
on the basis of local amino acid composition, flexibil-
ity, hydropathy, charge, coordination number and
several other factors [34,52,54–56]. A signature of a
probable ID region is the presence of low sequence
complexity coupled with amino acid compositional

bias, characterized by a low content of bulky hydro-
phobic amino acids (Val, Leu, Ile, Met, Phe, Trp and
Tyr), which typically form the cores of folded globular
proteins, and a high proportion of particular polar
and charged amino acids (Gln, Ser, Pro, Glu, Lys and,
on occasion, Gly and Ala) [57,58]. These attributes
were used to train artificial neural networks using
standard back propagation methods to develop a
series of ‘predictors of naturally disordered regions’
(PONDRÒs) [52,55,56,59,60].
That intrinsic protein disorder is a common phenom-
enon is illustrated by the fact that thousands of proteins
in the Swiss-Prot database were identified by PONDRÒ
as having long regions of sequence that share distin-
guishing sequence attributes with known ID regions
[55]. Furthermore, computational studies revealed that
eukaryotes exhibit more disorder than either prokaryo-
tes or archaea [34,60–62]. For example, in 22 bacterial
and seven archaebacterial proteomes, the percentage of
proteins with predicted regions of disorder ranged from
7% to 33% and from 9% to 37%, respectively. In con-
trast, in five eukaryotes, disorder ranged from 36% to
63% [60]. This large jump in the percentage of proteins
with long predictions of disorder in nucleated, rather
than non-nucleated, organisms was both remarkable
and unexpected. To explain this and similar observa-
tions, it was hypothesized that the higher abundance of
intrinsic disorder in eukaryotes could be a consequence
of the increased need for cell signaling and regulation in
higher organisms [34,35,58,60,63,64].

Qualitatively, it seems reasonable that unstructured
proteins could serve as hubs, providing a simpler basis
for responding to changes in the environment as
compared to rigid proteins. For example, disordered
regions can bind partners with both high specificity
and low affinity [65], suggesting that disorder-based
signaling and regulatory interactions can be highly spe-
cific but be easily reversed. These capabilities meet the
fundamental requirements of signaling interactions –
specificity and reversibility [49] with minimal structural
requirements. Another crucial property of ID proteins
and regions that could contribute to their function in
signaling networks is binding diversity; i.e., their ability
to partner with many other proteins and other ligands,
such as nucleic acids [66]. This opens the possibility of
disordered regulatory regions that are capable of bind-
ing many different partners.
An interesting consequence of the capability of ID
proteins and regions to interact with different binding
partners is the potential for polymorphism in the
bound state. That is, such proteins could have com-
pletely different geometries in the rigidified structures
that are induced upon binding to different partners
[48]. This conjecture has been confirmed at atomic
resolution. Portions of axin and frequently rearranged
in advanced T-cell lymphoma protein (FRAT), which
possess negligible sequence similarity, both interact
with an intrinsically disordered loop of glycogen syn-
thase kinase 3 beta (GSK3b) that adopts ordered
structure upon binding [67]. The binding sites for the

two molecules on GSK3b overlap substantially in the
crystal structures solved for the axin–GSK3b and
FRAT–GSK3b complexes [67]. Furthermore, although
both bound peptides are primarily helical, their
detailed structures and interactions with GSK3b have
substantial differences [67]. The ability of GSK3b to
bind two different proteins with high specificity via the
same binding site is mediated by the conformational
plasticity of the loop formed by residues 285–299. In
the nonbound form of GSK3b [67], this loop is poorly
defined in the electron density map, suggesting that it
very likely occupies multiple conformations. However,
this loop is induced to accommodate one of two com-
pletely distinct well-ordered structures, each of which
is specific to the bound partner [67,68]. While some
residues in this versatile GSK3b binding site are
involved in interactions with both axin and FRAT,
Flexible nets A. K. Dunker et al.
5132 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
their local conformations in the bound state are differ-
ent. In addition, other residues are involved uniquely
with one ligand or the other [67].
By extending such detailed observations to protein–
protein interaction networks in general, we suggest
that a unique feature of disordered regions in hub pro-
teins could be structural plasticity in the unbound
state, which when combined with the capability of
interacting with multiple structurally distinct partners,
results in structural polymorphism in the bound state.
These features have very important functional implica-

tions. By this means, ID hub proteins and regions
could serve multiple and distinct signaling networks
and be regulated via different pathways. For example,
GSK3b plays a crucial role in the wingless-type
MMTV integration site family (Wnt) signaling path-
way by controlling the levels of b-catenin [69–71], and
GSK3b is also known to be involved in insulin and
growth factor signaling pathways [72–75]. GSK3b
functions as a signal transducer for these two com-
pletely independent pathways without any obvious
cross-talk or interference [67]. In the Wnt signaling
network, a subset of the cellular GSK3b pool is incor-
porated into a multiprotein complex that brings
GSK3b and its b-catenin substrate into close proxim-
ity. In the insulin signaling pathway, GSK3b operates
via a completely different mechanism, where the phos-
phorylation of Ser9 converts the disordered N-termi-
nus of GSK3b to an autoinhibitory segment that
blocks access to the active site substrate binding cleft
[76]. The functional segregation of the insulin and Wnt
signaling networks requires either the absence of an
exchange between the subsets of the cellular GSK3b
molecules involved in each pathway, or suggests
mutual exclusion of the two processes. That is, the
involvement of GSK3b with the axin–adenomatous
polyposis coli complex can reverse (via the action of
the specific phosphatases associated with the men-
tioned complex [77,78]) or override the inhibitory Ser9
phosphorylation present on a recruited GSK3b mole-
cule via the substantial enhancement in activity

towards b-catenin afforded by the axin ‘scaffolding’
[76]. Importantly for this minireview, GSK3b uses two
different ID regions to participate in two completely
unrelated pathways: the disordered N-terminal frag-
ment (residues 1–34) for insulin signaling and the dis-
ordered loop (residues 285–299) for the Wnt network.
Intrinsic disorder and protein–protein
interaction networks
The advantages of ID proteins and regions for forming
associations with multiple partners led us to search the
literature for hub proteins having detailed structural
characterization. Table 1 contains a few examples of
structurally characterized hub proteins that were iden-
tified. The range of structural types fell into three
broad classes (as indicated in the ‘Percentage disor-
dered’ column): entirely or mostly disordered (that is,
nearly 100% disordered), partially disordered (a mid-
range percentage of disorder) and entirely or mostly
ordered (nearly 0% disordered). For the mostly
ordered hub proteins, we paid specific attention to the
structure of the binding partners. In many cases, these
partners contained regions of intrinsic disorder. One
example of an ordered hub, calmodulin, makes use of
a flexible hinge to facilitate binding diversity. Each of
these classes and their roles in protein–protein inter-
action networks will be discussed in turn.
Mostly disordered hubs
Several hub proteins have been shown to be com-
pletely or almost completely disordered in solution,
including a-synuclein, caldesmon, high mobility group

protein A (HMGA), and synaptobrevin (Table 1). An
interesting, well-studied, illustrative example of this
group of hubs is provided by HMGA [formerly called
HMG-I(Y)], a founding member of a new protein class
called architectural transcription factors [79]. As dis-
cussed in more detail below, this protein has been
implicated in the development of cancer and several
other pathological conditions [80]. HMGA is consid-
ered a central hub of nuclear function, being able to
bind to at least 18 known protein partners as well as
to several specific DNA structures [80].
Both circular dichroism (CD) [81] and nuclear mag-
netic resonance (NMR) [82,83] indicate that HMGA
lacks structure, with the molecule exhibiting a random
coil-like structure over its entire length. The atypical
electrophoretic mobility of this molecule [84] also sug-
gests a high content of extended structure. Figure 2A
compares the results of PONDRÒ analysis by two pre-
dictors of intrinsic disorder, firstly, a predictor of intrin-
sic disorder that integrates various methods-based
predictor of long disordered regions and X-ray based N-
and C-terminal predictors (VL-XT) [52,57,59] and sec-
ondly, a length-dependent predictor of intrinsic protein
disorder (VSL1) [85]. While VL-XT is the most well-
characterized member of the PONDRÒ family, VSL1
is more accurate overall and, indeed, obtained the best
results of the 20 order ⁄ disorder predictors tested in the
6th Critical Assessment of Methods for Protein Struc-
ture Prediction (CASP6; />In complete agreement with the experimental data, the
predictor outputs in Fig. 2A indicate that the HMGA

A. K. Dunker et al. Flexible nets
FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5133
Table 1. Number of interacting protein partners for hub proteins. Experimentally characterized disorder is described in terms of the start and stop residues of the disordered region(s) and
overall percentage of disordered residues in the whole protein. The BIND, DIP and HPRD database query results are presented as number of protein–protein interactions ⁄ number of com-
plexes (inter. ⁄ comp.), while the STRING search results are presented as number of protein–protein interactions (inter.) only. Proteins are ordered by percentage of disorder. BIND, the Bio-
molecular Interaction Network Database ( [201]. DIP, the Database of Interacting Proteins ( [202]. STRING, Search Tool for the Retrieval of
Interacting Genes ⁄ Proteins (associations with confidence scores > 0.7) ( [203]. HPRD, the Human Protein Reference Database ( [204]. N.D., not
in database.
Protein
(accession number) Disordered regions (start-stop)
Percent
disordered
(%)
BIND
(inter. ⁄ comp.)
DIP
(inter. ⁄ comp.)
STRING
(inter.)
HPRD
(inter. ⁄ comp.) Illustrative partners
a-synuclein (P37840) 1–140 [154] 100 10 ⁄ 1 N.D. 27 27 ⁄ 0 parkin, tau, Ab [155]; 14-3-3, CaM [156]
Caldesmon (P12957) 1–771 [157] 100 235 ⁄ 35 N.D. 27 7 ⁄ 0 ERK [158]; S100a & b [159]; myosin, actin,
calmodulin [160]
HMGA (P17096) 1–107 [161] 100 485 ⁄ 33 1 ⁄ 0413⁄ 3 AP1, NF-jB, C ⁄ EBPb,Oct)1 [80]; HIPK2,
Sp1 [162]
Synaptobrevin (P63027) 1–96 [163]) 100 199 ⁄ 17 7 ⁄ 086⁄ 0 syntaxin 1 A & 1B [164]; BAP31 [165];
VAMP assoc. protein A [166];
VAMP assoc. protein B [167];
SNAP-25 [168]

BRCA1 (P38398) 170–1649 [169] 79 158 ⁄ 14 16 ⁄ 011976⁄ 8 p53, ATM, BRCA2, c-Myc [169];
Chk1 & 2 [170]
XPA (P23025) 1–102, 210–273 [113] 63 27 ⁄ 24⁄ 04112⁄ 0 RPA70, RPA34, ERCC1, TFIIH,
XAB1 & 2 [171]
Estrogen receptor
a (P03372)
1–184 [172] 31 69 ⁄ 612⁄ 011690⁄ 1 p53 [173]; BRCA1 [174]; TATA box
binding protein [172]; calmodulin,
c-Jun [175]
p53 (P04637) 1–73 [176]; 183–188, 224–227 [177];
291–312 [178]; 319–323, 357–360 [179]
29 1900 ⁄ 40 34 ⁄ 0 239 164 ⁄ 8 Mdm2, ATM, ERK, p38, BCL-X
L
[180]
Mdm2 (Q00987) 1–17 [181]; 17–24, 110– 125 [182];
210–304 [183]
26 95 ⁄ 411⁄ 07229⁄ 3 p53, ARF, ATM, CK2, HIF-1a [184]
Calcineurin, subunit A
(Q08209)
1–13, 390–414, 469–521 [185] 16 451 ⁄ 24 1 ⁄ 0315⁄ 1 NFAT [186]; calcipressin 1 [187];
cabin 1 [188]; SOCS-3 [189];
calsarcin [190]
14-3-3¢n (P63104) 68–77, 134–137, 230–245 [191]
a
12 29 ⁄ 61⁄ 09761⁄ 1 p53, Wee1, Tau, Raf-1 Cdc25C,
Bad [192]
Cdk2 (P24941) 36–46, 152–162 [121]
a
7322⁄ 611⁄ 012530⁄ 15 protein phosphatase 2 A, cyclin E1,
DNA polymerase alpha [193]; BRCA1,

cyclin A [194]
Actin (P68133) 1–7, 42–52 [195]
a
5 7359 ⁄ 457 13 ⁄ 03319⁄ 1 profilin [196]; deoxyribonuclease I,
vitamin D binding protein, thymosin
beta-4, cofilin [197]
Calmodulin (P62152) 77–81 [136] 3 2962 ⁄ 88 3 ⁄ 0950⁄ 0 neurogranin, calcineurin, AC1 [198];
calponin [199]; caldesmon [200]
a
Disordered regions were based on missing residues in the PDB entries listed in the Swiss-Prot entry.
Flexible nets A. K. Dunker et al.
5134 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
sequence has a high propensity for disorder over its
entire length. However, HMGA was shown to undergo
disorder-to-order transitions upon binding to DNA or
protein partners [83,86,87]. For example, the DNA-
binding regions of the HMGA assume a planar,
crescent-shaped configuration called the ‘AT-hook’
when specifically bound to the minor groove of short
AT-rich stretches of DNA [83,86,87].
Despite its lack of ordered structure, HMGA parti-
cipates in a wide variety of nuclear processes ranging
from modulation of chromosome and chromatin
mechanics [88,89] to acting as an architectural tran-
scription factor that regulates the expression of more
than 45 different eukaryotic and viral genes in vivo
[79,90]. Besides their association with whole chromo-
somes [88,89], HMGA proteins also bind to individual
nucleosomes, both in vitro and in vivo [91,92]. In addi-
tion to these unique DNA-binding characteristics, at

least 18 different transcription factors have been repor-
ted to specifically interact with HMGA proteins (sum-
marized in [80]). The list of proteins known to interact
with HMGA includes transcription factors such as
AP-1, ATF-2 ⁄ c-Jun heterodimer, IRF-1, c-Jun, NF-jB
p50 ⁄ p65 heterodimer, C ⁄ EBPb, Elf-1, NF-AT, NF-jB
A
B
C
Fig. 2. Order ⁄ disorder predictions on three
hub proteins. (A) PONDRÒ VL-XT (red) and
VSL1 (magenta) predictions on the human
HMGA protein sequence (Swiss-Prot acces-
sion number P17096). Green horizontal bars
correspond to the areas of the protein that
have been identified as the minimal regions
required for specific protein–protein interac-
tions with other transcription factors (after
[80]): 1, IRF-1; 2, ATF-1 ⁄ c-Jun; 3, NF-Y; 4,
SRF; 5, NF-jB; 6, p50; 7, Tst-1 ⁄ Oct-6; 8,
HIPK-2. Although only eight target proteins
are shown here, it has been established that
HMGA physically interacts with at least 18
transcription factors [80]. Dark yellow hori-
zontal bars correspond to the areas of the
protein (known as AT-hooks) that are
involved in DNA binding. A PONDRÒ
score ¼ 0.5 corresponds to a prediction of
disorder. (B) PONDRÒ VL-XT (red) and VSL1
(magenta) predictions on the Xenopus laevis

XPA protein sequence (Swiss-Prot acces-
sion number P27088). Vertical red and blue
bars correspond to the accessible and inac-
cessible trypsin digestion sites, respectively.
Notice how, for the most part, cut sites
within predicted ordered regions are not
cleaved by the trypsin. Green horizontal bars
correspond to the functional regions of XPA
and reflect interactions sites with the follow-
ing binding partners: 1, 34 kDa subunit of
RPA [103,104]; 2, ERCC1 [106,107]; 3,
70 kDa subunit of RPA [103,104]; 4, TFIIH
[108,109]; 5, XAB1 [101]. Dark yellow hori-
zontal bar corresponds to the minimal frag-
ment of XPA known to interact with
damaged DNA [110]. (C) PONDRÒ VL-XT
(red) and VSL1 (dark pink) predictions on the
human Cdk2 sequence (Swiss-Prot acces-
sion number P24941). Horizontal green bars
correspond to the regions of Cdk2 involved
in the interaction with p27
Kip1
(residues with
atoms within 5 A
˚
of atoms of p27
Kip1
[122]).
A. K. Dunker et al. Flexible nets
FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5135

p50 homodimer, NF-jB p65, NF-Y, Oct-1, Oct-2 A,
PIAS3, Pu.1, RNF4, SRF, and Tst-1 ⁄ Oct-6 hetero-
dimer [80].
HMGA gene expression is maximal during embry-
onic development [93] and has been suggested to be
involved in the control of cell growth and differenti-
ation [94]. Interestingly, overproduction of HMGA
can be oncogenic and promote tumor progression and
metastasis via dramatic alterations in numerous signa-
ling pathways [80]. Based on these observations, it was
suggested that HMGA proteins function in the cell as
highly connected ‘nodes’ of protein–DNA and pro-
tein–protein interactions that influence a diverse array
of normal biological processes including growth, pro-
liferation, differentiation and death [80].
In summary, HMGA is a well-studied hub protein
that in the absence of binding partners has been
characterized to be completely disordered by NMR
[82,83] and CD [81], supporting the hypothesis that
hub proteins are strong candidates to possess appreci-
able amounts of disorder. The HMGA example was
also a major factor leading to the suggestion that
hub proteins as a group might depend on intrinsic
disorder [49]. This supposition is supported below by
several additional examples of hub proteins that util-
ize ID regions in their associations with multiple
partners.
Partially disordered hub proteins
Table 1 also lists several hub proteins that possess an
intermediate range of both ordered and disordered seg-

ments (internal loops and ⁄ or terminal tails), including
BRCA1, Mdm2, XPA, p53, estrogen receptor a, and
subunit A of calcineurin. Disordered regions appear to
play important roles in the binding interactions of
these hub proteins. The xeroderma pigmentosum
group A protein (XPA) represents an interesting exam-
ple of a partially disordered hub protein.
Xeroderma pigmentosum is an autosomal recessive
human disease characterized by hypersensitivity to sun-
light and a high incidence of skin cancer on sun-
exposed skin [95,96]. This hypersensitivity is caused by
defects in the nucleotide excision repair (NER) path-
way that is necessary to correct many types of DNA
damage [95,97]. XPA, consisting of 273 amino acid
residues, participates in the assembly of the damage
recognition ⁄ incision complex, recruiting several other
functional proteins to the site of damage [98]. Partic-
ularly, it has been shown that this protein binds to
three NER factors; replication protein A (RPA),
exchange repair cross complementing complex 1
(ERCC1) and transcription factor IIH (TFIIH), as
well as to UV- or chemical carcinogen-damaged DNA
[99,100]. Furthermore, XPA was shown to interact
with the cytoplasmic GTPase XPA-binding protein 1
(XAB1) [101] and with a tetratricopeptide repeat pro-
tein XAB2 [102].
Functionally, XPA can be divided into several dis-
tinct regions (Fig. 2B): (a) the N-terminal fragment
(residues 4–29) that binds to a 34 kDa subunit of RPA
[103,104]; the basic amino acid region (residues 30–42)

that is responsible for localizing XPA in the nucleus
[105]; the acidic region (residues 78–84) that is import-
ant for interaction with ERCC1 [106,107]; and the
C-terminal region (residues 226–273) that binds to
TFIIH [108,109]. Furthermore, the central region (resi-
dues 98–219) is the minimal polypeptide that preferen-
tially binds damaged DNA [110]. Finally, the fragment
98–187 is necessary for binding to the 70 kDa subunit
of RPA [103,104]. Figure 2B presents the distribution
of the PONDRÒ VL-XT and VSL1 scores within the
XPA sequence and illustrates the long predicted disor-
dered regions at or near the two ends (from M1 to
A55 and from S63 to P88 at the N-terminus and from
L183 to E230 near the C-terminus). Importantly,
Fig. 2B shows that the central DNA-binding domain
is likely to be mostly ordered, whereas the multiple
protein binding sites are located in the regions that are
likely to be disordered.
In agreement with the predictions (Fig. 2B), NMR
solution structure of a human XPA fragment contain-
ing the minimal DNA-binding domain (residues
98–219), revealed that one-third of this molecule is dis-
ordered [111,112]. These conclusions were further con-
firmed by the results of limited proteolysis analysis
[113]: mild trypsin digestion produced cuts at 33 of the
possible 48 sites, with no cleavage at any of the 14
possible sites in the internal DNA-binding region
(Q85–I179) (Fig. 2B). The observed cleavage sites were
predominantly in two of the large regions of predicted
disorder [113]. In general, it is believed that cut sites

within disordered regions are more easily cleaved by
proteases than those found in structured areas
[114,115]. Thus, excellent agreement was observed
between PONDRÒ prediction of order and disorder
and the observed sites of protease resistance and sensi-
tivity, respectively [113].
Summarizing, XPA is an illustrative example of the
class of hub proteins that contain disordered tails
and ⁄ or loops as well as ordered regions. Importantly,
flexibility of the disordered fragments in such proteins
facilitates interactions with multiple binding partners
without sacrificing specificity and enhances the ability
of hub proteins to participate in multiple signaling
pathways.
Flexible nets A. K. Dunker et al.
5136 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
Ordered hubs interacting with disordered
partners
Table 1 lists four examples of protein hubs that are
mostly ordered: actin, calmodulin, Cdk2, and 14-3-3¢n.
Below, we describe how ordered hubs may interact
with intrinsically disordered binding partners, and how
such interactions may play crucial roles in regulation
and coordination of hub protein activities.
The orderly progression of cells through the various
phases of the cell cycle is governed by several distinct
cyclin-dependent protein kinases (Cdks), which there-
fore are considered as the master timekeepers of cell
division [116]. Unlike other protein kinases, Cdks are
regulated by binding to their cyclin protein partners,

forming active heterodimeric complexes. Eight Cdk
family members (Cdk1–Cdk8) and nine cyclins (A–I)
have been identified so far. Interestingly, each Cdk pairs
with a separate cyclin class, most of which have at least
two members [117,118]. For example, Cdk1 together
with cyclin B1 directs the G2 ⁄ M transition. Exit from
G1, in contrast, is primarily under the control of cyclin
D ⁄ Cdk4 ⁄ 6. Finally, two other cyclins (A and E) that
pair with Cdk2 are required for the G1 ⁄ S transition and
progression through the S phase [117,118].
All Cdks have similar sizes (30–40 kDa) and share at
least 40% sequence identity, including the highly con-
served 300 residue catalytic core. On the contrary, the
cyclin subunits vary in size (30–80 kDa), but all contain
a homologous 100 residue cyclin box domain. The Cdk
subunits are not catalytically active unless they bind to
a cyclin partner and form a basally active complex.
The fully active complex is produced when the Cdk is
phosphorylated [116,119]. Crystal structures of several
Cdks (phosphorylated and dephosphorylated) and their
complexes with cyclins and inhibitors have been solved
[119]. All Cdks have the same overall fold as other
eukaryotic protein kinases. For example, monomeric
Cdk2 consists of an N-terminal lobe rich in b-sheet (N
lobe), a larger C-terminal lobe rich in a-helix (C lobe),
and a deep cleft at the junction of the two lobes that is
the site of ATP binding and catalysis [120]. Figure 2C
represents distribution of the PONDRÒ VL-XT and
VSL1 scores within the human Cdk2 sequence and
illustrates that this protein is likely to be almost com-

pletely ordered, having only several small regions of pre-
dicted disorder. The crystal structure of human Cdk2
(PDB ID: 1URW) is consistent with this prediction,
with only two regions containing missing residues [121].
Interestingly, these two segments, residues 36–46 and
152–162, overlap with experimentally identified regions
of Cdk2 that interact with cyclin-dependent kinase inhi-
bitor protein p27/1B (p27
Kip1
) (Fig. 2C) [122].
Regulation of Cdk activity is essential for the proper
timing and coordination of numerous nuclear processes,
including DNA replication and chromosome separ-
ation, required for cell growth and division. Not surpris-
ingly, the activity of Cdks throughout the cell cycle is
precisely directed by a combination of several mecha-
nisms, including the control of cycle-dependent varia-
tions in the levels of activating partners, cyclins (via
regulation of their synthesis and degradation); coordi-
nation of Cdk phosphorylation and dephosphorylation,
which is required for controlled activation and deactiva-
tion of Cdks; and variations in the levels of the Cdk
inhibitor proteins, CKIs, responsible for the deactiva-
tion of the Cdk–cyclin complexes [116,119]. Four major
mammalian CKIs, which regulate cell proliferation
through their physical interactions in the nucleus with
Cdks, have been discovered so far. p21
Waf1 ⁄ Cip1 ⁄ Sdi1
and
p27

Kip1
inactivate Cdk2 and Cdk4 cyclin complexes by
binding to them, whereas p16
INK4
and p15
INK4B
are
specific for Cdk4 and Cdk6. Importantly, CKIs are able
to inhibit Cdk–cyclin activity by blocking formation of
active Cdk–cyclin complexes via binding to inactive
Cdk or by binding to the active complex [116,119].
The Cdk inhibitor p21
Waf1 ⁄ Cip1 ⁄ Sdi1
is important for
p53-dependent cell cycle control, mediating G1 ⁄ S
arrest through inhibition of Cdks and possibly through
inhibition of DNA replication [123]. A striking dis-
order-to-order transition for p21
Waf1 ⁄ Cip1 ⁄ Sdi1
upon
binding to one of its biological targets, Cdk2, was
demonstrated by proteolytic mapping, CD spectropo-
larimetry, and NMR spectroscopy [66]. In fact, it has
been established that p21
Waf1 ⁄ Cip1 ⁄ Sdi1
and its NH
2
-ter-
minal fragment, being active as Cdk inhibitors, lacked
stable secondary or tertiary structure in free solution.

However, the p21
Waf1 ⁄ Cip1 ⁄ Sdi1
NH
2
-terminus adopts
an ordered, stable conformation when bound to Cdk2
[66]. This intrinsically disordered nature probably
explains the ability of p21
Waf1 ⁄ Cip1 ⁄ Sdi1
to bind to and
to inhibit a diverse family of cyclin–Cdk complexes,
including cyclin A–Cdk2, cyclin E–Cdk2, and cy-
clin D–Cdk4 [124]. Thus, the intrinsic disorder of
p21
Waf1 ⁄ Cip1 ⁄ Sdi1
is associated with binding diversity
and helps to explain the role for structural disorder in
mediating binding specificity in biological systems [66].
The p27
Kip1
protein is another member of the p21
family of Cdk inhibitors that negatively regulate the
cell cycle and thereby contributes to cellular growth
and development [125,126]. p27
Kip1
contains an N-ter-
minal Cdk-inhibition domain and a C-terminal domain
of unknown function called the QT domain [125,127].
The crystal structure of the human p27
Kip1

Cdk-
inhibition domain (residues 22–106) bound to human
cyclin A–Cdk2 complex shows that residues 25–93 of
A. K. Dunker et al. Flexible nets
FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5137
p27
Kip1
bind in an ordered conformation comprising
an a-helix, a 3
10
helix, and b-structure [120]. Import-
antly, the p27
Kip1
Cdk-inhibition domain was shown
to lack an intramolecular hydrophobic core. Instead,
p27
Kip1
interacts with the cyclin A–Cdk2 complex as
an extended structure, being bound to both cyclin A
and Cdk2. On cyclin A, it interacts with a groove
formed by conserved cyclin box residues. On Cdk2,
p27
Kip1
binds and rearranges the amino-terminal lobe
and also inserts into the catalytic cleft, mimicking ATP
in the context of the cyclin A–Cdk2 complex [120]. In
contrast, the unbound p27
Kip1
Cdk-inhibition domain
is intrinsically disordered (natively unfolded) as shown

by both CD and NMR spectroscopy. The NMR spec-
tra lack chemical-shift dispersion and exhibit negative
peaks for the
1
H-
15
N heteronuclear nuclear Overhauser
effect [122,128,129]. Despite showing disorder before
binding its targets, p27
Kip1
has nascent, but transient,
secondary structure that may have a function in
molecular recognition [122].
The kinetic analysis of p27
Kip1
folding induced by its
binding to Cdk2–cyclin A complex revealed that this is
a sequential process initiated by binding to cyclin A,
which is accompanied by folding of 34 residues (as esti-
mated by a method suggested in [130]). The binding of
p27
Kip1
to Cdk2 leading to the inhibition of kinase
activity is much slower and is accompanied by the fold-
ing of 59 residues [122]. Based on these observations, it
was proposed that p27
Kip1
(and potentially other CKIs,
such as p21 and p57) function essentially as ‘molecular
staples’, wherein the ‘prongs’ of the staple (domains 1

and 2) are unstructured and flexible before binding
Cdk–cyclin complexes. The staple analogy is completed
by a linker helix (partially structured in the unbound
state) that connects the two prongs. This analogy is
illustrated in Fig. 3A, which presents a model for
p27
Kip1
binding to the Cdk2–cyclin A binary complex
and shows the importance of both preformed, but tran-
sient structure in the linker region and the flexible nat-
ure of domains 1 and 2 for the efficient functioning of
this protein [122]. Figure 3B shows that p27
Kip1
is pre-
dicted to be mostly disordered by both PONDRÒ VL-
XT and VSL1. Importantly, a region of predicted order
overlaps with the fragment of p27
Kip1
shown to contain
a significant amount of regular secondary structure in
its complex with Cdk2–cyclin A.
The above-mentioned sequential folding-upon-bind-
ing mechanism has been suggested to be crucial for the
selective inhibition of specific Cdk–cyclin complexes by
corresponding CKIs. Furthermore, p21
Waf1 ⁄ Cip1 ⁄ Sdi1
and p27
Kip1
target the cell cycle CDKs (Cdk1, Cdk2,
Cdk3, Cdk4 and Cdk6) but fail to bind and inhibit

Cdk5 and Cdk7 [124]. Although the surfaces of the Cdks
are not markedly different from each other and there-
fore cannot provide a basis for specificity, important dif-
ferences in surface residues of the cyclin partners of
these Cdks have been described [122]. Thus, p27
Kip1
and other CKIs may have evolved to first recognize and
bind these specificity-determining sites and then to fold
into an extended structure that extends to the kinase
subunit and consequently inhibits its activity [122].
Flexible linkers between functional domains
Calmodulin (CaM) is a 148 residue protein with four
calcium binding sites that serves to mediate extracellu-
larly induced Ca
2+
signaling within the cytosol. CaM
modulates the activity of a large number of enzymes
by direct binding, with both calcium-dependent [131]
and calcium-independent [132] binding modes, with
the former likely to be much more common than the
latter.
The regions bound by CaM are typically about 20
residues in length and mostly a-helical in nature
[133,134]. CaM binding targets exhibit limited
sequence identity and in many cases are nonhomolo-
gous, thus a mechanism that incorporates specificity
but permits diversity must be encoded in CaM’s struc-
ture. The X-ray crystal structure of CaM is dumbbell-
like with two homologous globular domains connected
by a rigid 26 residue a-helix [135]. Subsequent to the

X-ray structure determination, NMR analyses revealed
that residues 77–81 in the middle of the central helix
were highly flexible and functioned as a hinge [136].
This hinge facilitates a binding mode in which CaM
surrounds the target regions of its partners within the
two Ca
2+
-binding, globular domains, and in some
cases the hinge region remains unstructured after
association with the CaM target [132].
The interior faces of the globular domains have
features that accommodate target diversity, such as
nonrigid helix–helix packing that allow backbone
adjustments and high methionine contents that are
especially adept at side chain adjustments [132]. An
important structural feature enabling intermolecular
binding with maximal surface area of interaction is the
flexible connector between the two globular domains.
This flexibility accommodates a high diversity of
sequence features in the target by allowing the CaM
surface to seek complementary interactions by samp-
ling different positions and orientations relative to the
binding target surface. Additionally, the flexible hinge
facilitates variable separation of the two globular
domains after binding has occurred, again allowing for
binding diversity [136]. Despite the small size of the
disordered region (just five residues) and the slight
Flexible nets A. K. Dunker et al.
5138 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
amount of disorder in the entire protein (just 3%), this

disorder is crucial for the binding mechanism that
enables CaM to associate with multiple partners.
Overall, a wide range of diverse sequences are recog-
nized by CaM [133,134].
ID regions vs. dehydrons as the basis
for binding to multiple partners
The examples presented above emphasize the import-
ance of intrinsic disorder for hub protein function: ID
regions provide hubs with the ability to bind multiple
structurally diverse targets, thereby enabling them to
participate in and possibly regulate multiple networks.
An alternative conjecture has been made, namely that
the interactivity and connectivity of proteins of known
structure in proteomic networks depends on the num-
ber of dehydrons (backbone hydrogen bonds that are
insufficiently protected from the surrounding water
molecules) observed for each protein [137–142]. Dehy-
drons, the coulombic stabilizing energy of which
increases as water is excluded, are effectively adhesive,
A
B
Fig. 3. Disorder-to-order transition upon
binding for p27
Kip1
. (A) A model for p27
Kip1
binding to the Cdk2–cyclin A binary com-
plex. p27
Kip1
is yellow, Cdk2 (K2) cyan and

cyclin A (A) magenta. In these panels, the
subunit not present in the experimental
binding reactions is gray to emphasize the
relevance of experimental data for binary
binding reactions to the mechanism of bind-
ing the Cdk2–cyclin A complex (right).
(Modified from [122] with the permission of
the authors, ª Nature Publishing Group,
1998–2005.) (B) PONDRÒ VL-XT (red) and
VSL1 (dark pink) predictions on the human
p27
Kip1
sequence (Swiss-Prot accession
number P46527). Green horizontal bars cor-
respond to the regions of the protein
involved in interaction with Cdk2 and
cyclin A: 1, domain 1 interacting with
cyclin A; 2, a linker helix involved in binding
both cyclin A and Cdk2; 3, domain 2 inter-
acting with Cdk2 [122]. Blue and dark yellow
horizontal bars correspond to the helices
and b-structure stabilized by the formation
of a triple complex, cyclin A–Cdk2–p27
Kip1
.
A. K. Dunker et al. Flexible nets
FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS 5139
because association-induced local water removal
increases this hydrogen-bond stability [141]. Important
for the present work, the application of the dehydron

concept in an analysis of the evolution of the yeast
proteomic network [11,143], indicated that new binding
partnerships could be promoted by relaxing the struc-
ture of the packing interface, which effectively increa-
ses the number of dehydrons [144]. This was
interpreted to mean that the rate of increase in interac-
tome complexity (i.e., the rate that new connectivities
are adopted) coincides with the rate of increase of the
number of dehydrons. According to this conjecture,
proteins with greater numbers of dehydrons represent
more plausible molecules for hub evolution.
How did the previous authors carry out the analysis
of the yeast interactome and show that hub proteins
were enriched in dehydrons? First, they showed that
there is a statistical correlation between the local
PONDRÒ VL-XT score and the degree of protection of
the indicated hydrogen bond [144]. Based on this corre-
lation, PONDRÒ VL-XT scores were used to show that
increases in the number of apparent dehydrons represen-
ted a plausible basis for SFN evolution [144]. Although
the occurrences of dehydrons and intrinsic disorder
scores are correlated, it is not immediately evident which
characteristic – dehydrons or disorder – actually
explains the previous observations [144] and thus may
provide the basis for hub evolution. As reviewed in more
detail here and elsewhere [40,48], disordered regions
have been shown to be able to adopt different structures
upon interaction with structurally diverse partners
(thereby increasing the potential number of partners
that can be accommodated by a given protein segment),

but no evidence has yet been presented that dehydrons
have a similar capability. Thus, mutation-driven vari-
ation of locally disordered regions is more likely than
dehydrons alone to be one of the key structural factors
leading to the evolution of hub proteins.
How do ID regions work?
To explain one of the potential mechanisms used by ID
proteins to interact with their binding partners, the con-
cept of ‘molecular recognition element’ (MoRE) was
introduced [145,146]. The MoRE defines an interaction-
prone short segment of disorder that becomes ordered
upon specific binding. There are three basic types of
MoREs: those that form a-helical structures upon bind-
ing; those that form b-strands (in which the peptide
forms a b-sheet with additional b-strands provided by
the protein partner); and those that form irregular struc-
tures when binding [48,145–147]. Proposed names for
these structures are a-MoRE, b-MoRE, and I-MoRE,
respectively [48]. Of course, a given MoRE could be
composed of more than one type of secondary structure
type when bound to its partner, resulting in complex
MoREs, such as I-a-I-MoREs, a-b-MoREs, etc.
MoREs can be detected experimentally as segments
of disordered regions that maintain some residual
structure (as in the case of p27
Kip1
[122]). They also
can be discovered by analysis of protein–protein com-
plexes deposited in the Protein Data Bank (PDB) [148]
that consist of short nonglobular protein fragments

bound to a large globular partner [48,145–147]. Origin-
ally, 14 a-MoREs were described in 12 proteins [147].
However, subsequent, more detailed analysis of PDB
revealed more than 2500 such complexes, further ana-
lysis of which gave a nonredundant set of 372 nonho-
mologous MoREs. This dataset includes  10 000
residues, 27% of which were assigned to a-helical con-
formation, 12% were b-structural and 48% were irre-
gular, whereas the remaining 13% of the residues were
disordered (based on missing coordinates in the PDB
files) (A. Mohan, P. Romero, C.J. Oldfield, V.N. Uver-
sky & A.K. Dunker, unpublished data). Overall, this
analysis shows that complexes of short nonglobular
protein fragments bound to large globular proteins are
rather abundant in PDB, and thus in nature.
Alternatively, MoREs can be detected computation-
ally [146,147]. In fact, for several proteins it was noted
that PONDRÒ VL-XT could identify experimentally
characterized binding regions as short downward spikes
(signifying propensity for order) flanked by strong pre-
dictions of disorder [145,149,150]. By combining addi-
tional sequence-derived criteria to the downward spike
indications from PONDRÒ analysis, we developed an
algorithm that identifies regions of high a-MoRE pro-
pensity [146,147]. Application of this predictor to several
genomes revealed that, while somewhat less than 3% of
bacterial and archaea proteins contain regions of high
a-MoRE propensities, about 23% of eukaryotic pro-
teins in general, and almost 50% of eukaryotic regula-
tory proteins in particular, contain a-MoRE signals

[146,147]. This study suggests that disorder-to-helix
transitions are common in protein interaction networks,
but laboratory experiments are needed to test whether
this mechanism is indeed as common as suggested.
Concluding remarks
Systematic postgenome proteome-wide analysis of pro-
tein interactions using large-scale two-hybrid screens
suggest that these interactions can be described as
complex SFNs [9,26,151]. On the other hand, many
traditional approaches have been developed to analyze
interactions, coordination, signaling and regulation on
Flexible nets A. K. Dunker et al.
5140 FEBS Journal 272 (2005) 5129–5148 ª 2005 FEBS
a smaller scale (e.g. within the scope of a single or
multiple interacting pathways). Quite often these
small-scale approaches yielded interesting results that
were used to develop models that correctly predicted
outcomes of changes and interruptions within the sys-
tem studied. Integrating small-scale network informa-
tion with global network information represents an
important but difficult task. Successful completion of
this task could lead to improved quality of the overall
data, shed light on the mechanisms of timing and regu-
lation, indicate how global and local properties of
complex macromolecular networks affect observable
biological properties (phenotype) and functions (physi-
ology) and suggest how changes in such properties
contribute to human diseases. Several groups are pro-
ductively working on the task of understanding inter-
esting subnetworks [152,153]. The example of GSK3b,

in which different regions of intrinsic disorder are inde-
pendently involved in different regulatory pathways,
suggests that identifying and cataloguing ID regions in
hub proteins could provide a useful approach for
organizing protein–protein interaction subnetworks
into larger networks.
We have presented evidence that ID proteins can
play crucial functional roles in protein interaction net-
works. This evidence indicates how some hubs carry
out function, how some local nets are integrated into
global networks, and how multiple processes can be
coordinated, regulated, timed and isolated from one
another. The information available at this stage in the
developing field of systems biology provides strong evi-
dence for the existence of extensive complex networks
combining global and local properties, but also dem-
onstrates that much more data will be needed to
develop reliable models. Carrying out a comparison of
the number of interactions involving at least one disor-
dered partner with the number of interactions invol-
ving structured partners for a few selected subnetworks
would be very useful. Such a comparison would pro-
vide an estimate of the fraction of protein–protein
interactions that utilize ID protein regions and the
fraction that involve only structured proteins. While
we anticipate that disorder-to-order transitions might
be very common in hub protein interactions because of
the binding diversity advantage that disorder provides,
the extracted ratio would provide an estimate of the
frequency that this property is utilized on global scales.

Studies along these lines are currently in progress.
Acknowledgements
We thank A.L. Barabasi for permission to modify
published figures and R.W. Kriwacki for assistance
with figure modification. We also thank Tanguy Le
Gall and Molecular Kinetics, Inc. for the use of the
proprietary fragment-mapping program to mine PDB
for some of the data presented in Table 1. The Indiana
Genomics Initiative (INGEN) and NIH Grant 1 R01
LM007688-0A1 provided support to A.K.D. This
work was supported in part by INTAS 2001-2347
Grant to V.N.U., and L.M.I. was supported by the
grant MCB-0444818 from the National Science Foun-
dation.
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