T-RMSD: a fine-grained, structure-based classification method and its application to the functional characterization of TNF receptors.

Abstract

This study addresses the relation between structural and functional similarity in proteins. We introduce a novel method named tree based on root mean square deviation (T-RMSD), which uses distance RMSD (dRMSD) variations to build fine-grained structure-based classifications of proteins. The main improvement of the T-RMSD over similar methods, such as Dali, is its capacity to produce the equivalent of a bootstrap value for each cluster node. We validated our approach on two domain families studied extensively for their role in many biological and pathological pathways: the small GTPase RAS superfamily and the cysteine-rich domains (CRDs) associated with the tumor necrosis factor receptors (TNFRs) family. Our analysis showed that T-RMSD is able to automatically recover and refine existing classifications. In the case of the small GTPase ARF subfamily, T-RMSD can distinguish GTP- from GDP-bound states, while in the case of CRDs it can identify two new subgroups associated with well defined functional features (ligand binding and formation of ligand pre-assembly complex). We show how hidden Markov models (HMMs) can be built on these new groups and propose a methodology to use these models simultaneously in order to do fine-grained functional genomic annotation without known 3D structures. T-RMSD, an open source freeware incorporated in the T-Coffee package, is available online.

Full text

Page 1

T-RMSD: A Fine-grained, Structure-based Classification
Method and its Application to the Functional
Characterization of TNF Receptors
Cedrik Magis1⁎, François Stricher1, Almer M. van der Sloot1,
Luis Serrano1,3and Cedric Notredame2
1EMBL/CRG Systems Biology
Research Unit Center for
Genomic Regulation (CRG),
UPF, Barcelona, Catalunya,
08003, Spain
2Bioinformatics and Genomics
program, Center for Genomic
Regulation (CRG), UPF,
Barcelona, Catalunya, 08003,
Spain
3Institucio Catalana de Recerca I
Estudis Avancats (ICREA)
professor, Center for Genomic
Regulation (CRG), Barcelona,
Catalunya, 08003, Spain
Received 18 March 2010;
received in revised form
21 April 2010;
accepted 7 May 2010
Available online
13 May 2010
This study addresses the relation between structural and functional
similarity in proteins. We introduce a novel method named tree based on
rootmeansquaredeviation(T-RMSD),whichusesdistanceRMSD(dRMSD)
variations to build fine-grained structure-based classifications of proteins.
ThemainimprovementoftheT-RMSDoversimilarmethods,suchasDali,is
its capacity to produce the equivalent of a bootstrap value for each cluster
node. We validated our approach on two domain families studied
extensively for their role in many biological and pathological pathways:
the small GTPase RAS superfamily and the cysteine-rich domains (CRDs)
associated with the tumor necrosis factor receptors (TNFRs) family. Our
analysis showed that T-RMSD is able to automatically recover and refine
existing classifications. In the case of the small GTPase ARF subfamily,
T-RMSD can distinguish GTP- from GDP-bound states, while in the case of
CRDs it can identify two new subgroups associated with well defined
functional features (ligand binding and formation of ligand pre-assembly
complex). We show how hidden Markov models (HMMs) can be built on
these new groups and propose a methodology to use these models
simultaneously in order to do fine-grained functional genomic annotation
without known 3D structures. T-RMSD, an open source freeware incorpo-
rated in the T-Coffee package, is available online.
© 2010 Elsevier Ltd. All rights reserved.
Edited by M. Sternberg
Keywords: structural classification; functional classification; multiple
sequencealignment;TumorNecrosisFactorReceptor;Cysteine-richDomain
Introduction
Fold identification is a veryactive field of research,
whose importance reflects the usefulness of struc-
tural information when elucidating protein
function.1It is therefore not surprising to note that
several structural classifications have been devel-
oped over time.2–4These resources, which are either
used in a stand alone fashion or through their
integration within domain databases,5tend to be
focused on the identification of generic folds. In
practice, these resources are mostly used to help
assigning an anonymous sequence to some high-
level fold/functional category. The subtle differ-
ences associated with fine-grain functional and
structural variations are often overlooked. Refining
such classification and showing that fine-grain
structural clustering can have clear functional
implications is precisely the object of the work
presented here.
We have developed a novel method named tree
based on RMSD (T-RMSD), which computes a
clustering using a combination of structure compar-
ison and tree reconstruction methods. The aim of the
model is to identify subtle structural differences
within a group of homologous protein sequences or
domains using distance RMSD measures (dRMSD)
as a similarity metrics. Contrarily to the standard
*Corresponding author. E-mail address:
cedrik.magis@crg.es.
Abbreviations used: T-RMSD, tree based on RMSD;
dRMSD, distance RMSD; CRD, cysteine-rich domain;
TNFR, tumor necrosis factor receptor.
doi:10.1016/j.jmb.2010.05.012 J. Mol. Biol. (2010) 400, 605–617
Available online at www.sciencedirect.com
0022-2836/$ - see front matter © 2010 Elsevier Ltd. All rights reserved.

Page 2

RMSD that measures the distance between super-
posed Cαequivalents, dRMSD6relies on a compar-
ison between homologous intramolecular distances.
It is therefore independent of any structural super-
position, and depends only on how equivalences are
defined.Dali7is probablythe most popular dRMSD-
based superposition procedure. Interestingly, the
metricscanbeusedtoevaluateMSAsindependently
of any reference or superposition,8,9a property that
makes it an ideal measure for comparing the
structural merits of alternative alignments using a
meta-aligner such as M-Coffee10or 3D-Coffee.11
Existing dRMSD-based metrics, however, can be
used only to estimate a global structural similarity,
thus making it hard to properly quantify the
reliability of clusters built upon them. Here, we
show that one can take advantage of the distance-
based nature of the dRMSD to build distinct clusters
on each individual column of an alignment, thus
producing a collection of alternativeclusters that can
becompared,combinedandassessedforrobustness,
inawayreminiscentofbootstrapsupportanalysisin
phylogeny. In practice, a bootstrap value is associ-
ated with each node. It indicates the fraction of
ungapped columns supporting the split associated
with the considered node (100 indicates a full
support from all considered columns). This ap-
proach, outlined in Fig. 1, forms the backbone of
the T-RMSD.
Clustering strategies are mostly exercises in
description, and their full power materializes only
when clusters prove useful to reveal functional
relationships. One of the main limitations when
estimating the relative merits of fine-grain classifiers
is the scarcity of experimental data lending itself to
such analysis. Indeed, one needs a family of
sequences with several well characterized members
both from a structural and a functional point of
view. One also requires these members to be
sufficiently functionally distinct to allow an estima-
tion of how well a clustering manages to correlate
functional and structural variations. In practice, the
Fig. 1. A schematic display of the T-RMSD procedure. A multiple sequence alignment is used to declare equivalences
among aligned residues. Each sequence must have a known 3D structure. For a given ungapped position (P1) and a given
sequence A, the Cα–Cαdistance between the residue at position P1 and every other residue of A (occurring in an
ungapped column) is estimated on the 3D structure. A similar set of distances is measured on structure B. The divergence
between A and B is estimated by summing the absolute differences between equivalent pairs on A and B. A divergence
matrix can be computed for position P1 by considering every possible pair of sequence. Such matrices are computed for
every ungapped position of the MSA and are then turned into a UPGMA tree. The collection of trees thus obtained is
combined into a consensus tree using the consense program of the PHYLIP package (see Methods).
606
T-RMSD: A Method for Structure Classification

Page 3

analysis of the Pfam12domain database (containing
N11,000 families) reveals about 100 protein families
associated with sufficient structural information
(N20 distinct UniProt ID). These families include
protein-binding domains (PDZ, SH2 and SH3),
nucleic acid-binding protein (RNA recognition
motifs, zinc fingers and U-box) and enzymes
(protein kinases, histone acetyltransferases and
proteases). Unfortunately, and although a large
literature corpus is available for many of these
families, the required information is not yet inte-
grated with appropriate levels of detail and the
establishment of reference datasets such as those
used here remains a labor-intensive process. In the
context of this work, we have focused the validation
of our method (T-RMSD) on two well-described
domain families: the small GTPase RAS super-
family13and the cysteine-rich domains (CRDs)
associated with tumor necrosis factor receptors14
(TNFRs). Both of these domain families have in
common the availability of a large number of
experimental 3D structures (between 20 and 50
non-redundant structures), along with enough
experimental data to support the existence of
significantly different functional subgroups.
These two families also define the two extremes of
our functional/structural spectrum. On the one
hand, RAS, a super family made of related small
GTPases with subcategories defined well enough at
the sequence level to be recovered using profile
collections. These subcategories are associated un-
ambiguously with distinct features such as cellular
localization, pathway and conformational switches
related to GTP hydrolysis (Table 1). Altogether, this
makes the GTPase RAS superfamily an ideal
validation target. The CRDs, on the other hand,
exhibit a less clear functional/structural relation-
ship. CRDs constitute the ligand-binding region of
the TNFRs, a class of receptors involved in many
key cellular processes (cell survival, programmed
cell death, proliferation) and whose association with
important pathologies is well documented (cancer,
viral infection, autoimmune disease).15–17While
TNFRs exhibit very different signaling capacities,
sizes and a variety of domain organization, most of
them seem to use similar binding modes.18Because
of its biological importance, this process has
received a lot of attention,19–23although the precise
determinants shaping CRD specificity toward a
given TNFL remain to be determined. Sequence
analysis alone brings little information, with the
main domain databases (Pfam, Smart24and
PROSite25) showing, on average, 40% agreement
with UniProt annotation.26This disagreement
should not come as a surprise given the wide
sequence diversity measured across CRDs (about
18±8% of identity). The current CRD classification is
therefore structure-based27,28and relies on a module
decomposition of each domain (N and C terminal),
with each module making up about half of a CRD.
This approach conveniently describes all existing
CRDs but it fails at defining functionally homo-
geneous groups and is therefore not really informa-
tive. Furthermore, the most common module
combination puts together CRDs that can be
structurally diverse (RMSD up to 5 Å) and
probably just as functionally different, given their
tendency to occur in any part of the domain
architecture. In the context of this work, one of
our goals was to assess whether systematic
structure-based clustering can yield new more
homogeneouscategories.Theresultingmethodology
is very generic and can be applied to any protein
family for which the definition of structural sub-
groups makes biological sense (provided enough
structural data are available).
Table 1. Small GTPase RAS superfamily functional classification
Domain PDBID (%) Function/LocalizationReference
RAS946±10 Specific association with plasma membrane, Golgi or ER
after post-translational modification.
Rho GTPases are key regulators of cytoskeletal dynamics
and affect many cellular processes, including cell polarity,
migration, vesicle trafficking and cytokinesis
Organise membrane trafficking processes, vesicle budding,
uncoating, mobility and fusion
Ran is located predominantly in the nucleus of eukaryotic
cells and is involved in the nuclear import of proteins as well as
in control of DNA synthesis and of cell-cycle progression,
nuclear localization, and its lack of sites required for
post-translational lipid modification
Recruitment of proteins or complexes to initiate vesicle budding.
Regulation of vesicle biogenesis in intracellular
traffic/Phospholipase D activator
Cytoskeletal remodeling via RHO interaction, voltage-gated
calcium channel activity modulation.
Undefined
25
RHO5 52±13
26
RAB19 42±927
RAN1n.a. 28
SAR
ARF
0
6
n.a.
48±10
29
29,30
RGK1 n.a.31
Ambiguous
Overall
2 n.a.
29±12
—
50
The RAS superfamily is dissected into seven domain families, each assuming a specific function and cellular localization. For the overall
superfamily, as for each subfamily, the average identity and its standard deviation are indicated, calculated for the sequences included in
our reference set.
607
T-RMSD: A Method for Structure Classification

Page 4

Results
Validation of T-RMSD for functional
classification of RAS subfamilies
We first estimated the relevance of the structure-
based clustering produced by the T-RMSD (Fig. 1;
Materials and Methods) on the classification of the
RAS GTPase subfamilies (Table 1).29-35For that
purpose, non-redundant sequences with a known
structure (Supplementary Data Table 1) were
multiply aligned with 3D-Coffee and the resulting
MSA was used to derive a neighbor-joining (NJ) tree
(Supplementary Data Fig. 2A). As expected, this
clustering roughly coincides with the widely accept-
ed RAS classification,13with the exception of
centaurin γ1 (2IWR) and centaurin γ3 (3IHW).
This does not come as a surprise because these two
proteins are hard to classify, which is reflected by
the disagreement among domain databases. The
same 3D-Coffee MSA was then fed into T-RMSD in
order to produce a structure-based clustering
(Fig. 2b). The topology thus obtained is roughly
similar to the sequence-based analysis with one
notable exception, its capacity to distinguish the
GTP-bound from the GDP-bound states of the ARF
subfamily.36,37The split separating these two sub-
groups (ARF-GTP and ARF-GDP) has a bootstrap
value of 38, which means that 38% of the columns
support this dichotomy. However, discrimination
cannot be achieved on the basis of sequence alone
because each RAS GTPase is able to bind both GTP
and GDP. This example therefore illustrates well the
advantage of a structure-based clustering over its
sequence-based counterpart. Moreover, obtaining
the reliability value (bootstrap) also shows the
benefits of basing an analysis on the simultaneous
use of many possible models (one per column)
rather than relying on a global measure. It is worth
mentioning that discrimination of GTP and GDP
forms could be achieved only on the ARF subfamily,
which suggests a more concerted conformational
change related to GTP or GDP binding than the rest
of the GTPases of the RAS superfamily.
Structure-based classification of CRDs
associated with the TNFR family
We applied the same procedure to the CRD
dataset. In that context, the goal was to validate the
method and to estimate whether the current classi-
ficationcouldberefinedtobecomemoreinformative.
A set of 22 non-redundant sequences was identified
(see Materials and Methods) and multiply aligned
using 3D-Coffee. The resulting MSA was used to
computetheunrootedneighbor-joiningphylogenetic
tree shown in Fig. 3a. In theory, such a tree could be
used to reveal subclasses (as happens with the RAS
family) but in practice the evolutionary distance
between the considered domains is too great,
resulting in an unrooted tree poorly resolved with
no significant bootstrap support for any deep node
(Fig.3a).Furthermore,the subgroupsit definesshow
no significant agreement with the current module
based classification.27,28
The current CRD classification is based on the
dissection and comparison of five PDB structures
suggesting nine different modules (A1, A2, B1, B2,
C2, X2, D2, N and N1), the combination of which can
be used to describe eight CRD types. The 22 CRDs
considered in the dataset are predominantly A1-B2
types (underlined in red in Fig. 3a). They appear to
be evenly spread and associated with all the other
module combinations such as A1-B1, A2-B1, A1-C2,
A1-D2 or X2-N. Similar results were obtained even
when excluding columns containing gaps. When
applying the T-RMSD analysis to the same dataset
(Fig. 3b), we obtain a much more informative
topology, clearly supporting three well defined
groups with one outlier. The bootstrap values
indicate the fraction of ungapped columns (13 in
total) supporting a given split and suggest a good
support for these three groups. For instance, the
separation between group II and the other CRDs is
supported by 100% of ungapped columns, while
53% support the separation between group I and III.
Interestingly, the groups thus defined are fairly
homogeneous from a module point of view, with
groups I and II composed almost exclusively by A1-
B2 CRDs and group III perfectly overlapping a
previously described CRDs category: the small
TNFR (made of one or two CRDs).38We compared
these results with those obtained when using the
Dali Z-score as a clustering metrics (Materials and
Methods) and found the resulting clusters to be in
broad agreement (Supplementary Data Fig. 1) even
though theylack the bootstrap value associated with
the T-RMSD clusters.
Structural characterization of the new
CRDs classification
The differences captured here, which clearly
reflect genuine structural variation, might be anec-
dotal and non-biologically informative. In order to
rule out this possibility we performed a detailed
analysis of the clusters, exploring all three layers of
information associated with the considered
sequences: homology, structure and function. Our
goal was to establish whether each cluster is
specifically associated with a structural or functional
property.
Eachsubgroupwasrealignedusing3D-Coffeeand
analyzed for sequence conservation. As expected,
the degree of conservation is fairly poor aside from a
fewhighlyconservedresidues:onehighlyconserved
aromatic/hydrophobic position whose involvement
in the domain packing has been previously de-
scribed through structural analysis19,21and the
disulphide bridges network (C1–C2, C3–C5 and
C4–C6). Nonetheless,itis interesting to note how the
three groups identified at the structural level might
have been easily defined on the basis of their
insertion-deletion (indel) patterns (Fig. 4a). For
608
T-RMSD: A Method for Structure Classification

Page 5

Fig. 2. Tree analysis of current
sequence/function clustering of
small GTPase RAS superfamily
compared to a new clustered based
classification. (a) Phylogenetic
analysis of the 43 structures of the
non redundant GTPase RAS (see
Methods).Theneighbor-joiningtree
was derived from MSA of CRDs
using 3D-Coffee. For clarity, boot-
strap values under 10.0 are not indi-
cated on the nodes. (b) T-RMSD
structural clustering derived from
MSA obtained by 3D-Coffee. RAB
domains are indicated in light
violet, RAS domains in light blue,
ARF domains in light green,
orphans domains (RAN and RGK)
in light orange, RHO in light red
and ambiguous domains in khaki.
609
T-RMSD: A Method for Structure Classification

Page 6

instance, in group I, the block defined by the C2–C5
cysteines is fairly compact and does not seem to
tolerate insertions or deletions. By contrast, the
corresponding region within groups II and III is
much more variable. Conversely, the C5–C6 block of
group II is more constrained than its counterparts in
groups IandIII.Asaconsequence, theoverall length
is fairly conserved within each group but variable
across groups. Group II is the largest, 5–10 residues
longer than groups I and III (Table 2). Interestingly,
the very nature of this variability probably explains
the failure of sequence-based clustering methods,
whose metrics are based on identity rather than a
comparison of indel patterns.
We then asked if such conservation patterns
could reflect structural homogeneity. We measured
the average standard RMSD in each group (Table
2) and found them highly homologous with
average values below 1.0 Å within each group
(see Materials and Methods). This observation is
not surprising given the structure-based nature of
the clustering, but it nonetheless confirms the
adequacy of dRMSD as a distance metric. As
suggested by their common A1-B2 module com-
position, groups I and II contain the most similar
members (Fig. 4b, left and middle respectively),
with precisely conserved topologies (loop 1 & 2,
turn 1 & 2). Despite a smaller structural core (black
dotted lines in Fig. 4b, right) group III is a bit more
diverse albeit homogeneous enough to also yield a
standard RMSD of less than 1.0 Å.
Homology-based validation of the new
CRD classification
Another way to estimate the biological meaning of
our three clusters is to assess the discriminative
power of sequence homology models based upon
Fig. 3. Tree analysis of current module-based CRD classification compared to a new clustered based classification. (a)
Phylogenetic analysis of the 22 structures of non redundant CRDs (see Methods). The neighbor-joining tree was derived
from MSA of CRDs using 3D-Coffee. (b) T-RMSD structural clustering derived from MSA obtained by 3D-Coffee. For a
and b, each CRD is represented by its PDB identifier, followed by its position number within the architecture of its
corresponding TNFR and the chain identifier of the corresponding PDB file. The module composition is indicated by
colored underlines: A1-B1, dark red; A1-B2, red; A1-C2, orange; A1-D2, yellow; X2-N, light green; and A2-B1, dark green.
Bootstrap values are given for each node.
Fig. 4. Sequence and structure analysis of CRD structural groups (a) MSAs of CRDs type I, II and III obtained from 3D-
Coffee. Alignments are illustrated using JalView 2.4 and conserved residues are colored from light blue to dark blue.
Conserved cysteine (dark blue columns) residues connectivity is indicated by black lines. Structural elements described
after structural superposition (Fig. 3b) are indicated under each alignment. Consensus sequences indicated for each type
were derived from MSAs. (b) Structural superimposition of CRDs according to their structural type I, II or III. CRD
structures are represented as ribbon and the disulfide bridge is shown as a black stick. CRDs are colored using a rainbow
scale according to the alphabetical order given by the PDB names and the position of the domain in the corresponding
TNFR architecture (from 1D4V-2 to 2HEY-4). Structurally variable regions are circled by broken black lines and
structurally conserved regions are circled by black dotted lines.
610
T-RMSD: A Method for Structure Classification

Page 7

Fig. 4 (legend on previous page)
611
T-RMSD: A Method for Structure Classification

Page 8

them. In order to do this analysis, the MSAs
associated with each of the three groups were
turned into hidden Markov model (HMM) profiles
(Materials and Methods) and used to scan the CRD
database, the rationale being that the HMM giving
the best score is more likely to define the true nature
of considered CRDs. This methodology was vali-
dated beforehand using a leave-one-out strategy to
assess each HMM specificity/sensitivity (Table 3).
The leave-one-out was done by removing in turn
each sequence from its corresponding MSA, re-
estimating the associated HMM and measuring the
E-value of the left out sequence when aligned with
the HMMs of the three groups (i.e. the re-estimated
HMM and the other two). When applied in turn to
each of the 22 CRDs this validation shows that 100%
of the sequences achieve a better score (lower E-
value) when aligned to the HMM corresponding to
their original group (Table 3). This high specificity is
unlikely to result from any bias in sequence
similarity given the similar level of identity across
the three CRD types (28±7%) or within each of the
three groups (26–33%).
The three HMMs built on the full dataset for
each group were used to annotate the 80 remain-
ing human sequences annotated as CRDs or
atypical CRDs in UniProt but for which no
experimental structure is available. Domains with
an E-value greater than 0.1 with each of the
HMMs of the three groups were left unclassified
(22 in total). The remainder were labeled (i.e.
assigned to group I, II or III) by the HMM yielding
the lowest E-value (Supplementary Data Table S2).
Newly and non-ambiguously automatically classi-
fied CRDs were then incorporated in the groups
MSAs and used to update their respective HMM
profiles (Fig. 5). The three HMMs thus produced
were used to label all the CRDs with UniProt
sequences annotated as TNFRs. In 98% of the
cases, orthologous CRDs received an identical
label across all vertebrate species.
Functional evaluation of the new classification
The three CRD groups identified through struc-
tural analysis were analyzed for known functions
associated with their members. CRD repeats have
been shown to play distinct roles in TNFRs. For
instance, the N-terminal CRD (known as the pre-
ligand assembly domain or PLAD) has a very
specific feature that sets it apart from other CRDs:
it does not necessarily interact with the ligand but
forms a specific pre-ligand assembly complex (homo
Table 2. Characterization ofCRDs according tostructural
type.
Sequence characteristics Group IGroup IIGroup III
Overall size
Average identity (%)
Core block length
Core block length
after superposition
Ratio sizea(%)
Core block RMSD (Å)
35±2
33±4
18
16.0±1.2
40±2
30±7
22
19.3±1.9
31±3
26±7
13
10.5±2.8
44.8±5.0
0.64±0.13
48.4±6.2
0.66±0.32
34.2±11.2
0.75±0.75
Overall size corresponds to the number of residues within CRDs
(average). Core block size corresponds to the number of
structurally conserved residues. The Core block/Overall ratio
corresponds to the ratio of the number of structurally conserved
residues (Core block) to the total number of residues (Overall,
average). Structural similarity of core blocks estimated by average
RMSD values, indicated (in Å) for each cluster (see Methods).
aCore block/Overall.
Table 3. Leave-one-out strategy performed on CRD sequences using HMM profiles built from MSA according to
structural clustering described earlier
Domain information
E value
Name PDBGene Domain Gr IGr II Gr III
TNF-R1
Ox40
HVEM
NGFR
CRME
TNF-R1
TNF-R1
Ox40
Ox40
DR5
DR5
HVEM
NGFR
NGFR
NGFR
CRME
TNF-R1
TACI
BCMA
TWEAKR
BAFF-R
1EXT
2HEY
1JMA
1SG1
2UWI
1EXT
1EXT
2HEY
2HEY
1D4V
1D4V
1JMA
1SG1
1SG1
1SG1
2UWI
1EXT
1XU1
1OQD
2RPJ
1OQE
TNFRSF1A
TNFRSF4
TNFRSF14
TNFRSF16
CRME
TNFRSF1A
TNFRSF1A
TNFRSF4
TNFRSF4
TNFRSF10B
TNFRSF10B
TNFRSF14
TNFRSF16
TNFRSF16
TNFRSF16
CRME
TNFRSF1A
TNFRSF13B
TNFRSF17
TNFRSF12A
TNFRSF13C
1
1
1
1
1
2
3
2
4
2
3
2
2
3
4
2
4
1
1
1
1
3,7E-05
3,5E-07
5,9E-07
4,2E-08
5,9E-06
1,4E-03
1,4E-03
2,2E-05
N0,1
6,2E-05
3,7E-03
1,3E-05
1,1E-02
2,8E-05
4,2E-03
N0,1
N0,1
N0,1
3,1E-02
N0,1
N0,1
6,4E-05
1,2E-03
5,4E-05
3,1E-06
8,2E-05
1,3E-14
2,7E-06
6,1E-10
1,7E-06
3,3E-13
3,6E-08
8,9E-12
5,1E-07
3,9E-08
9,7E-16
2,1E-05
N0,1
N0,1
1,7E-02
N0,1
N0,1
N0,1
N0,1
1,0E-01
N0,1
9,1E-04
4,0E-03
9,5E-03
N0,1
N0,1
N0,1
N0,1
2,3E-03
N0,1
N0,1
N0,1
N0,1
7,0E-02
2,2E-08
8,6E-05
3,7E-02
3,1E-02
CRDs are indicated by the commonname of the receptor they belong to, the PDB file used for the structural analysis, the gene name of the
corresponding receptor and the position in the receptor architecture from N to C terminus. Non-significant E values are N0.1.
612
T-RMSD: A Method for Structure Classification

Page 9

or hetero receptor–receptor complexes), prerequisite
in the TNFL binding process.39The existence of such
domains, which are essential for binding, has been
shown experimentally in several human TNFRs,
including CD40, TNFR1, TNFR2 and FAS.40,41The
core CRDs, however, are directly involved in ligand
binding activities as revealed by the structural
analysis of TNFR/TNFL complexes (Materials and
Methods). The annotation resulting from the scan-
ning of human TNFRs with our new collection of
CRD HMMs is shown in Fig. 5. A total of 88% of the
type I CRDs occur on the N terminus, thus labeling
the termination of 56% of the large receptors (three
to six CRDs). In contrast, type II CRDs are found
predominantly (about 92%) in the TNFR core. In
agreement with the literature, we found type III to
be mostly associated with small TNFRs made of one
or two CRD repeats for which a single domain is
sufficient for a high level of interaction specificity.38
These findings suggest that the two new subcate-
gories identified within the A1-B2 module group
correspond to two genuinely distinct classes of
CRDs: TNFL binders and PLAD CRDs. Unassigned
CRDs present us with the possibility that other
categories might exist, yet unreported for lack of
structural information.
Discussion
Here, we describe T-RMSD, a novel method for
fine-grained structural classification. Given an MSA
where each sequence has a known structure, the T-
RMSD uses dRMSD measurements to estimate a
Fig. 5. Classification of human putative CRDs associated with TNFRs according to the structural types I, II and III
defined here. Type I, red box; type II, blue box; and type III, green box. Unclassified CRDs are represented as a black box.
Half CRDs are identified by smaller boxes.
613
T-RMSD: A Method for Structure Classification

Page 10

clustering for each column of the MSA. The resulting
clusters are combined into one unique consensus
cluster, whose nodes are scored for their frequency
among the combined clusters. We show that this
algorithm can recover existing and validated classi-
fications (small GTPase RAS superfamily) or allow
the refinement of existing classifications (CRD
family). But more importantly, we show that these
fine-grain structural classes correspond to well-
defined functional variations, which justify the
systematic usage of this approach for refined
domain annotation.
The T-RMSD could be described as DALI
clustering with bootstrap values. It is therefore
not surprising in any way that our method
delivers accurate results, but it must be pointed
out that providing meaningful reliability values for
cluster nodes is just as important as providing a
clustering in the first place. Our approach provides
reliability estimation, and it gives an indication of
which intramolecular distances support better a
given split in the clustering (i.e. the column setting
apart one subgroup from the others). We do not
use this information in the current work, but it
could certainly find important applications in
homology modeling and drug design. The clusters
produced by T-RMSD are very precise and
reliable. Even though it is fairly obvious that
large families can be divided into smaller sub-
families, we show here that these refined classifi-
cations are sophisticated usage of structural
information, and that they make biological sense,
probably because the structure/function relation-
ship extends deep, at least within the small
GTPase RAS superfamily and CRD family.
Validation is achieved on the RAS superfamily.
We show how the T-RMSD recovers the known
classification and manages to precisely identify
within ARF the structural differences associated
with the two states of GTPase proteins: GTP and
GDP binding. We also tested the behavior of the
T-RMSD on a more challenging dataset: the TNFR
CRDs. As illustrated by the lack of a clear
consensus among domain databases and large
literature corpus, classifying CRDs is a challeng-
ing endeavor. Our results suggest that the T-
RMSD approach is able to recover a classification
roughly similar to that produced by Dali. How-
ever, the bootstrap support makes it possible to
use well supported deep branching nodes to
define three groups unambiguously and in a
non-arbitrary way. One difficulty when classifying
CRDs stems from the poor support offered by
sequence similarity. In order to validate our new
classification, we therefore used all the available
information associated with the considered
sequences. We first showed that their alignment
reveals well conserved spacing between disul-
phide bridges, in a way that appears to be group
specific. We showed that all subgroups considered
are reasonably homogenous from a structural
point of view. In order to further build on this
descriptive validation, we looked into the predic-
tive power of the three newly defined groups. We
did so by estimating an HMM for each group and
by establishing the preference of each HMM for
its group members. While this provided clear
support for the notion of the homogeneity within
each group, the strongest support for the new
categories presented here probably stems from the
capacity of their associated HMMs to identify
CRDs by functional criteria, such as PLAD (type
I), ligand binding capacity (type II) or small
TNFR-associated CRDs (type III). Although this
third category appears to be the most heteroge-
neous, detailed structural analysis reveals that all its
members exhibit a highly conserved hairpin struc-
ture known to interact with a conserved binding
pocket present in both APRIL and Baff (Baff/Baff-
R, Baff/BCMA, APRIL/BCMA and APRIL/TACI
co-crystals). This finding is consistent with the
reported capacity of several small TNFRs to cross-
react (TACI, Baff-R and BCMA) with their cognate
ligands (APRIL and Baff).42In Fig. 5, most of the N-
terminal CRDs are labeled as type I, but it is worth
mentioning that several of N-terminal domains
could not be labeled by any of the three HMMs.
This observation raises the possibility that further
expansion of the classification might be needed,
either by defining a new typology or by expanding
the current one. Unfortunately, the T-RMSD ap-
proach cannot help here, due to a lack of structural
information, but these observations might provide
an incentive to prioritize these proteins for structure
determination.
To conclude, our detailed validation of T-RMSD
on the small GTPase and the CRD classifications
provide a new basis for using structural and
functional information when performing genome
annotation at a detailed functional level. It must be
stressed that expanding the validation reported
here would be mostly hampered by the lack of
functional evidence. All known large families are
easily clustered using our approach, but when
doing so, one will often be at a loss to estimate
whether these clusters make any biological sense.
We have shown here that structure-based clusters
were biologically meaningful on two well studied
cases and we are now planning to explore further
in this direction by applying this methodology to
other important protein families, such as metabolic
enzymes, protein-binding domains involved in
signaling pathways and DNA-binding proteins.
Materials and Methods
Hardware and software
All experiments ran on a standard 2 GHz dualcore PC
with 4 Gbytes of RAM. The T-RMSD is incorporated in the
T-Coffee package, an open source freeware†.
†www.tcoffee.org
614
T-RMSD: A Method for Structure Classification

Page 11

Multiple sequence alignment and phylogenetic
tree computation
Multiplesequencealignmentswerecarriedoutusingthe
T-Coffee43alignment package (version 7.68). Structure-
based multiple sequence alignments were done with the
3D-Coffee mode of T-Coffee that makes it possible to
combine alternative pairwise structural and sequence
alignment methods.113D-Coffee was used to combine
three different types of aligners: SAP (structural aligner),
slow_pair (global aligner) and lalign_id_pair (local
aligner). For each sequence, the corresponding PDB
was used as template. This alignment was used to
compute a neighbor-joining44phylogenetic tree with 100
bootstrap replicates using the PHYLIP package‡.45
Phylogenetic and structural trees were represented
using Phylodendron version 0.8d.
Structural clustering using DaliLite version 3.0
Structural clustering of CRDs was done with DaliLite
version 3.0. The Z-scores obtained from pairwise com-
parison using DaliLite v3.0 were normalized to generate a
symmetrical square matrix of distances. The matrix was
then used to generate a structural tree using the
NEIGHBOR executable (an implementation of Neighbour
Joining and of the UPGMA methods) from the PHYLIP
package.
Structural clustering method: T-RMSD
A schematic display of the T-RMSD method is
presented in Fig. 1. The structural clustering is obtained
by comparing equivalent Cα–Cαintramolecular distances
across the MSA of the considered structures. Two
distances are considered equivalent when they separate
a pair of equivalent residues. The list of residue
equivalences is obtained from the alignment. Given a
pair of aligned sequences I and J and the two aligned
residues Icand Jc, a global distance is associated with this
pair by summing the difference of distances between
every pair d(IcIx) and d(JcJx), where Ix and Jx are two
aligned residues of the corresponding sequence (Eq. (1)).
The distance is then normalized by N, the number of
contributing columns (i.e. the number of ungapped
position x considered)
Dc I;J
ðÞ =
X
L
x
jd IcIx
ðÞ− d JcJx
ð Þj =N
Using this formula, one can estimate, on position c, a
distance for every pair of sequences (provide there is no
gap in the alignment column corresponding to position c).
A set of such distances defines a distance matrix. Such
matrices can be turned into clusters using an appropriate
clustering algorithm. In the context of this work, we used
the UPGMA algorithm to turn the matrices into unrooted
trees. With C ungapped columns, one produces C trees.
These trees are combined into one unique consensus tree
using the Consense program (PHYLIP). Consense produces
a consensus tree and labels each node with the number of
trees supporting the considered split. This value can be
normalized by C to indicate the fraction of columns
supporting a given node. The T-RMSD procedure is
implemented in T-Coffee (version 7.68 and higher).
Dataset of GTPase protein/domains
The small GTPase RAS superfamily is currently divided
into six different domain subfamilies corresponding to
specific functional differences: RAB, RAS, RHO, RAN,
SAR and ARF as defined by SMART and PROSITE and
RGK defined only by PIRSF.46We used this clustering as a
basis for the evaluation of the T-RMSD on this super-
family. Structures associated with small GTPases were
extracted from the Protein Data Bank47(PDB§) and
selected by several criteria related to structure quality
and non-redundancy: (1) only X-ray structures were used;
(2) presenting a full structural coverage (no missing or
undefined regions); (3) exhibiting resolution below 2.5 Å
(with one exception at 2.65 Å); and (4) showing a limited
sequence/structure redundancy using a threshold of
70%.48We thus defined a reference set of 43 domains
(Supplementary Data Table S2): 19 RAB domains, nine
RAS domains, six ARF domains, five RHO domains, one
RAN, one RGK and no SAR domain because no structure
presented full structural coverage. We included in this
dataset two additional GTPases for which domain
databases exhibit disagreement: AGAP2_HUMAN and
AGAP3_HUMAN (centaurin γ1 and centaurin γ3), which
contain a GTPase domain (Pfam: MIRO, PROSITE: RAB,
SMART: small GTPase, and Pfam: MIRO, PROSITE: RAS,
SMART: small GTPase, respectively).
Dataset of TNFR protein/domains
For sequence analysis, CRDs are referred to by the gene
name corresponding to the receptor they belong to and by
the number corresponding to their position within the
receptor architecture (N-terminal to C-terminal).
Sequences of CRDs associated with TNFR were obtained
from UniProt annotation (called TNFR-Cys). It includes
current described domain families corresponding to TNFR
associated CRDs, unidentified CRDs, and half CRDs
suggested in the current structural classification. Within
UniProt, a few of these half-CRDs are annotated as
atypical or truncated TNFR-Cys (from human,
TNFRSF4-3, TNFRSF12A-1 and TNFRSF13C-1). However,
a few CRDs and half CRDs are not described precisely
regarding domain database or structural classification. For
such cases, sequence boundaries were determined on the
basis of consensus sequences of structural modules (from
human, TNFRSF6B-5, TNFRSF14-4, TNFRSF19L-1 and
TNFRSF19L-3) or according to domain databases (from
Vaccinia virus, CRME-1, CRME-2 and CRME-3). A total of
97 CRDs or half CRDs were identified in the human TNFR
family.
All structures of CRDs associated with TNFRs (from
Homo sapiens, Rattus norvegicus and Vaccinia virus) used for
analysis and classification performed in this study were
extracted from the PDB. The selection of PDB files
corresponding to similar domains was realized on the
basis of the highest sequence coverage and highest
resolution. PDB files corresponding to TNFRs (gene/
receptor name) used in this work are: TNFRSF1A/TNF-
‡Felsenstein, J. (2005). PHYLIP (Phylogeny Interference
Package) version 3.6. Distributed by the author. Departement
of Genome Science, University of Washington, Seattle
§www.pdb.org
615
T-RMSD: A Method for Structure Classification

Page 12

R1, 1EXT;49TNFRSF4/Ox40, 2HEY;50TNFRSF10B/DR5,
1D4V;51TNFRSF12A/TWEAK-R, 2RPJ;52TNFRSF13B/
TACI, 1XU1;42TNFRSF13C/Baff-R, 1OQE;38TNFRSF14/
HVEM, 1JMA;53TNFRSF16/NGFR, 1SG1;54TNFRSF17/
BCMA, 1XU2;42CRME/CRME, 2UWI.55
Structural comparison of CRD subgroups
Global structural comparison using RMSD measure-
ment was achieved after defining the structural core block
withineachsubgroup.Thesestructuralcoreblocksinclude
positions likely to be aligned across all pairwise structural
alignment constructed using SAP version 0.5.56RMSD
values, after core blocks superimposition, were obtained
withthealigncommandofPyMOLpackageversion1.1∥.57
The number of aligned residues (despite the exclusion
routine of the PyMOL align command) is globally similar
with standard deviations between one and three residues
(Table 2). Our results are presented by subgroup: average
RMSD value, standard deviation and number of position
aligned for each structural core block.
HMM profile for sequence classification
Hidden Markov models (HMM) have proven to be
efficient for protein classification based on statistical
relevance of specific position. We used HMMer version
2.0 to generate profiles corresponding to each structural
subgroup we defined.58,59Profiles were built using the
default mode, which corresponds to local alignments
regarding domain sequences and global regarding the
HMM. Each profile was calibrated empirically in order
to increase sensitivity of searches using default para-
meters provided by HMMer version 2.0. Searches using
HMM profiles were achieved on a reduced dataset
corresponding to CRDs associated specifically with
TNFRs.
Acknowledgements
This work was supported by European Union
(EU) programs TRIDENT (grant number LSHC-CT-
2006-037686), 3D REPERTOIRE (grant number
LSHG-CT-2005-512028) and the Spanish Ministry
of Education and Science through its Consolider
programme. C.N. is supported by the CRG and the
Plan Nacional (BFU 00419) A.M. v.d. S. was funded,
in part, by a Juan de la Cierva fellowship of the
Spanish Ministry of Education and Science. The
authors wish to thank referees 1 and 2 for useful
comments and suggestions.
Supplementary Data
Supplementary data associated with this article
can be found, in the online version, at doi:10.1016/
j.jmb.2010.05.012
References
1. Sadowski, M. I. & Jones, D. T. (2009). The sequence-
structure relationship and protein function prediction.
Curr. Opin. Struct. Biol. 19, 357–362.
2. Orengo,C.A.,Michie,A.D.,Jones,D.T.,Swindells,M.B.
& Thornton, J. M. (1997). CATH: a hierarchic classifica-
tionofproteindomainstructures.Structure,5,1093–1108.
3. Murzin, A. G., Brenner, S. E., Hubbard, T. & Chothia,
C. (1995). SCOP: a structural classification of proteins
databases for the inverstigation of sequence and
structure. J. Mol. Biol. 247, 536–540.
4. Dietmann, S., Park,J.,Notredame, C.,Heger, A.,Lapp,
M. & Holm, L. (2001). A fully automatic evolutionary
classification of protein folds: Dali Domain Dictionary
version 3. Nucleic Acids Res. 29, 55–57.
5. Hunter, S., Apweiler, R., Attwood, T. K., Bairoch, A.,
Bateman, A., Binns, D. et al. (2009). InterPro: the
integrative protein signature database. Nucleic Acids
Res. 37, D224–D228.
6. Nishikawa, K. & Ooi, T. (1974). Comparison of
homologous tertiary structures of proteins. J. Theor.
Biol. 43, 351–374.
7. Holm, L. & Sander, C. (1993). Protein structure
comparison by alignment of distance matrices.
J. Mol. Biol. 233, 123–138.
8. O'Sullivan, O., Zehnder, M., Higgins, D., Bucher, P.,
Grosdidier, A. & Notredame, C. (2003). APDB: a novel
measure for benchmarking sequence alignment meth-
ods without reference alignments. Bioinformatics, 19,
i215–i221.
9. Armougom, F., Moretti, S., Keduas, V. & Notredame,
C. (2006). The iRMSD: a local measure of sequence
alignment accuracy using structural information.
Bioinformatics, 22, e35–e39.
10. Wallace, I. M., O'Sullivan, O., Higgins, D. G. &
Notredame, C. (2006). M-Coffee: combining multiple
sequence alignment methods with T-Coffee. Nucleic
Acids Res. 34, 1692–1699.
11. O'Sullivan, O.,Suhre, K., Abergel, C., Higgins, D. G. &
Notredame, C. (2004). 3DCoffee: combining protein
sequences and structures within multiple sequence
alignments. J. Mol. Biol. 340, 385–395.
12. Finn, R. D., Tate, J., Mistry, J., Coggill, P. C., Sammut,
J. S., Hotz, H. R. et al. (2008). The Pfam protein families
database. Nucleic Acids Res. 36, D281–D288.
13. Wennerberg, K., Rossman, K. L. & Der, C. J. (2005). The
RAS superfamily at a glance. J. Cell Sci. 118, 843–846.
14. Bazan, J. F. (1993). Emerging families of cytokines and
receptors. Curr. Biol. 3, 603–606.
15. Hehlgans, T. & Pfeffer, K. (2005). The intriguing
biology of the tumour necrosis factor/tumour necro-
sis factor receptor superfamily: players, rules and the
games. Immunology, 115, 1–20.
16. Balkwill, F. (2009). Tumour necrosis factor and cancer.
Nat. Rev. Cancer, 9, 361–371.
17. Locksley, R. M., Killeen, N. & Lenardo, M. J. (2001).
The TNF and TNF receptor superfamilies: integrating
mammalian biology. Cell, 104, 487–501.
18. Zhang, G. (2004). Tumor necrosis factor family ligand-
receptor binding. Curr. Opin. Struct. Biol. 14, 154–160.
19. Idriss, H. T. & Naismith, J. H. (2000). TNFα and the
TNF receptor superfamily: structure-function rela-
tionship(s). Microsc. Res. Tech. 50, 184–195.
20. Cha,S.S.,Sung,B.J.,Kim,Y.A.,Song,Y.L.,Kim,H.J.,
Kim, S. et al. (2000). Crystal structure of TRAIL-DR5
complex identifies a critical role of the unique frame
insertion in conferring recognition specificity. J. Biol.
Chem. 275, 31171–31177.
∥www.pymol.org
616
T-RMSD: A Method for Structure Classification

Page 13

21. Fellouse, F. A., Li, B., Compaan, D. M., Peden, A. A.,
Hymowitz, S. G. & Sidhu, S. S. (2005). Molecular
recognitionbyabinarycode.J.Mol.Biol.348,1153–1162.
22. Lam, J., Nelson, C. A., Ross, F. P., Teitelbaum, S. L. &
Fremont, D. H. (2001). Crystal structure of TRANCE/
RANKL cytokine reveals determinants of receptor-
ligand specificity. J. Clin. Invest. 108, 971–979.
23. Hymowitz, S. G., Compaan, D. M., Yan, M., Ackerly,
H., Dixit, V. M., Starovasnik, M. A. & de Vos, A. M.
(2003). The crystal structure of EDA-A1 and EDA-A2:
splice variants with distinct receptor specificity.
Structure, 11, 1513–1520.
24. Schultz, J., Milipetz, F., Bork, P. & Ponting, C. P.
(1998). SMART, a simple modular architecture re-
search tool: identification of signaling domains. Proc.
Natl Acad. Sci. USA, 27, 229–232.
25. Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., Cuche,
B., De Castro, E. et al. (2007). The 20 years of PROSITE.
Nucleic Acids Res. 36, D245–D249.
26. UniProt Consortium. (2009). The Universal Protein
Resource (UniProt) 2009. Nucleic Acids Res. 37,
D169–D174.
27. Naismith, J. H. & Sprang, S. R. (1998). Modularity in
theTNF-receptorfamily.TrendsBiochem.Sci.23,74–79.
28. Bodmer, J. L., Schneider, P. & Tschopp, J. (2002). The
molecular architecture of the TNF superfamily. Trends
Biochem. Sci. 27, 19–26.
29. Hancock,J.F.(2003).Rasproteins:differentsignalsfrom
a different locations. Nat. Rev. Mol. Cell Biol. 4, 373–384.
30. Etienne-Manneville, S. & Hall, A. (2002). Rho GTPases
in cell biology. Nature, 420, 529–635.
31. Stenmark, H. (2009). Rab GTPases as coordinators of
vesicle traffic. Nat. Rev. Mol. Cell Biol. 10, 513–525.
32. Sazer, S. & Dasso, M. (2000). The RAN decathlon:
multiple roles of RAN. J. Cell Sci. 113, 1111–1118.
33. Kahn, R. A., Cherfils, J., Elias, M., Lovering, R. C.,
Munro, S. & Schurmann, A. (2006). Nomenclature for
the human Arf family of GTP-binding proteins: ARF,
ARL, and SAR proteins. J. Cell Biol. 172, 645–650.
34. Donaldson, J. G. & Honda, A. (2005). Localization and
function of Arf family GTPases. Biochem. Soc. Trans.
33, 639–642.
35. Kelly, K. (2005). The RGK family: a regulatory tail of
small GTP-binding proteins. Trends Cell Biol. 15,
640–643.
36. Vetter, I. R. & Wittinghofer, A. (2001). The guanine
nucleotide-binding switch in three dimensions.
Science, 294, 1299–1304.
37. Pasqualato, S., Renault, L. & Cherfils, J. (2002). Arf,
Arl, Arp and Sar proteins: a family of GTP-binding
proteins with a structural device for ‘front-back’
communication. EMBO Rep. 3, 1035–1041.
38. Liu, Y., Hong, X., Kappler, J., Jiang, L., Zhang, R.,
Xu, L.etal. (2003).Ligand-receptorbindingrevealed by
the TNF family member TALL-1. Nature, 423, 49–56.
39. Chan, F. K., Chun, H. J., Zheng, L., Siegel, R. M.,
Bui, K. L. & Lenardo, M. J. (2000). A domain in TNF
receptors that mediates ligand-independent receptor
assembly and signaling. Science, 288, 2351–2354.
40. Chan, F. K. (2000). The pre-ligand binding assembly
domain: a potential target of inhibition of tumour
necrosis factor receptor function. Ann. Rheum. Dis. 59,
i50–i53.
41. Chan, F. K. (2007). Three is better than one: pre-ligand
receptor assembly in the regulation of TNF receptor
signaling. Cytokine, 37, 101–107.
42. Hymowitz, S. G., Patel, D. R., Wallweber, H. J.,
Runyon, S., Yan, M., Yin, J. et al. (2005). Structures of
APRIL-receptor complexes: like BCMA, TACI
employs only a single cysteine-rich domain for
high affinity ligand binding. J. Biol. Chem. 280,
7218–7227.
43. Notredame, C., Higgins, D. G. & Heringa, J. (2000).
T-coffee:a novel method for fast andaccurate multiple
sequence alignment. J. Mol. Biol. 302, 205–217.
44. Nei, M. & Saitou, N. (1987). The neighbor-joining
method: a new method for reconstructing phylo-
genetic trees. Mol. Biol. Evol. 4, 406–425.
45. Felsenstein, J. (2005). PHYLIP (Phylogeny Interference
Package) version 3.6. Distributed by the author.
Departement of Genome Science, University of
Washington, Seattle.
46. Nikolskaya, A. N., Arighi, C. N., Huang, H., Barker,
W. C. & Wu, C. H. (2007). PIRSF family classification
system for protein functional and evolutionary analy-
sis. Evol. Bioinform. 10, 197–209.
47. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G.,
Bhat, T. N., Weissig, H. et al. (2000). The Protein Data
Bank. Nucleic Acids Res. 28, 235–242.
48. Kosloff, M. & Kolodny, R. (2008). Sequence-similar,
structure-dissimilar protein pairs in the PDB. Proteins:
Struct. Funct. Genet. 71, 891–902.
49. Naismith, J. H., Devine, T. Q., Kohno, T. & Sprang,
S. R. (1996). Structures of the extracellular domain of
the type I tumor necrosis factor receptor. Structure,
4, 1251–1262.
50. Compaan, D. M. & Hymowitz, S. G. (2006). The
crystal structure of the costimulatory OX40-OX40L
complex. Structure, 14, 1321–1330.
51. Mongkolsapaya, J., Grimes, J. M., Chen, N., Xu, X. N.,
Stuart, D. I., Jones, E. Y. & Screaton, G. R. (1999).
Structure of the TRAIL-DR5 complex reveals mechan-
isms conferring specificity in apoptotic initiation. Nat.
Struct. Biol. 6, 1048–1053.
52. He, F., Dang, W., Saito, K., Watanabe, S., Kobayashi,
N., Güntert, P. et al. (2009). Solution structure of the
cysteine-rich domain in Fn14, a member of the tumor
necrosis factor receptor superfamily. Protein Sci. 18,
650–656.
53. Carfí,A.,Willis,S.H.,Whitbeck,J.C.,Krummenacher,
C., Cohen, G. H., Eisenberg, R. J. & Wiley, D. C. (2001).
Herpes simplex virus glycoprotein D bound to the
human receptor HveA. Mol. Cell, 8, 169–179.
54. He, X. L. & Garcia, K. C. (2004). Structure of the nerve
growth factor complexed with the shared neuro-
trophin receptor p75. Science, 304, 870–875.
55. Graham, S. C., Bahar, M. W., Abrescia, N. G., Smith,
G. L., Stuart, D. I. & Grimes, J. M. (2007). Structure of
CrmE, a virus-encoded tumour necrosis factor recep-
tor. J. Mol. Biol. 372, 660–671.
56. Brown, N. P., Orengo, C. A. & Taylor, W. R. (1996).
Aproteinstructurecomparisonmethodology.Comput.
Chem. 20, 359–380.
57. DeLano, W.L. (2002). The PyMOL Molecular Graphics
System on World Wide Web www.pymol.org.
58. Durbin, R., Eddy, S., Krogh, A. & Mitchinson, G.
(1998). The theory behind profile HMMs. Biological
Sequence Analysis: Probabilistic Models of Proteins
and Nucleic Acids, pp. 101–133, Cambridge Universi-
ty Press, Cambridge, UK.
59. Eddy, S. (1998). Profile hidden Markov models.
Bioinformatics, 14, 755–763.
617
T-RMSD: A Method for Structure Classification