Vol. 25 no. 15 2009, pages 1862–1868
CORAL: aligning conserved core regions across domain families
Jessica H. Fong∗and Aron Marchler-Bauer
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health,
8600 Rockville Pike, Bethesda, MD 20894, USA
Received on December 10, 2008; revised on May 5, 2009; accepted on May 21, 2009
Advance Access publication May 26, 2009
Associate Editor: Burkhard Rost
Motivation: Homologous protein families share highly conserved
sequence and structure regions that are frequent targets for
comparative analysis of related proteins and families. Many protein
families, such as the curated domain families in the Conserved
Domain Database (CDD), exhibit similar structural cores. To improve
accuracy in aligning such protein families, we propose a profile–
profile method CORAL that aligns individual core regions as gap-free
Results: CORAL computes optimal local alignment of two profiles
with heuristics to preserve continuity within core regions. We
benchmarked its performance on curated domains in CDD, which
have pre-defined core regions, against COMPASS, HHalign and
PSI-BLAST, using structure superpositions and comprehensive
curator-optimized alignments as standards of truth. CORAL improves
alignment accuracy on core regions over general profile methods,
returning a balanced score of 0.57 for over 80% of all domain families
in CDD, compared with the highest balanced score of 0.45 from
other methods. Further, CORAL provides E-values to aid in detecting
homologous protein families and, by respecting block boundaries,
Availability: CORAL will be included in future versions of the
NCBI Cn3D/CDTree software, which can be downloaded at
Supplementary information: Supplementary data are available at
Homologous protein families contain core regions that reflect
conservation in molecular evolution. Many protein family
including blocks, or ungapped regions, within an alignment. The
Conserved Domain Database (CDD) (Marchler-Bauer et al., 2009)
models protein domains explicitly as series of blocks. For NCBI-
curated domains, the blocks represent structural core motifs based
on structure superpositions as well as conserved sequence regions
and motifs. Comparative analysis of proteins and protein families
through sequence alignment is invaluable for grouping homologs,
∗To whom correspondence should be addressed.
subdividing diverse families into sub-families, tracing evolutionary
histories and identifying conserved functional sites.
In recent years, alignment methods that compare two profiles,
the statistical models that represent protein families, have been
shown to improve alignment quality and homolog recognition over
sequence–sequence methods such as BLAST (Altschul et al., 1997)
and sequence–profile methods such as PSI-BLAST (Altschul et al.,
1997; Schaffer et al., 2001). Numerous profile alignment methods
have been assessed in Edgar and Sjolander (2004), Heger and
Holm (2001), Ohlson and Elofsson (2005), Ohlson et al. (2004),
Panchenko (2003), Rychlewski et al. (2000), Soding (2005), Yona
and Levitt (2002) and others. While many alignment methods
focus on detecting remote homologs in order to expand coverage
of functional inference, obtaining high-quality alignments remains
difficult even for closely-related families. According to structure
superpositions, corresponding core regions in many homologous
domains differ by fewer insertions and deletions than inferred by
general alignment programs, reflecting the stability of the structural
a method CORAL (CORe ALigner) to align core regions from
two protein families without indels within blocks, which we will
refer to as the core constraint. CORAL is implemented through
a common dynamic programming engine for optimal pair-wise
alignment (Needleman and Wunsch, 1970; Smith and Waterman,
Several other algorithms to align sequence or sequence profiles
to core regions have been effective for detecting similarities
or assigning domains. These algorithms include a profile–profile
method using Gibbs sampling (Panchenko, 2003), and SALTO
(Kann et al., 2005) and GLOBAL(Kann et al., 2007) which employ
set of complete blocks and GLOBAL aligns a sub-set (including
full or empty set) of contiguous columns within every block. All
of these methods disallow indels in alignments of blocks and
exclude sequence regions outside blocks. Additionally, LAMA
(Pietrokovski, 1996) and CYRCA (Kunin et al., 2001) were
developed to align individual blocks that represent sequence motifs
(Henikoff et al., 2000). Block shift and extension operations have
through REFINER (Chakrabarti et al., 2006).
Here, we present the CORAL algorithm and benchmark its
performance on curated domains in CDD against other widely
used profile methods COMPASS (Sadreyev and Grishin, 2003),
HHalign (Soding, 2005) and PSI-BLAST. Reference alignments
are inferred from structure superpositions from the VAST database
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
set (Van Walle et al., 2005), and from a comprehensive set of
expert-determined mappings, and homology is defined by CDD
relationships. In particular, CORAL outperforms all other methods
in the quality of alignments. We also discuss the role of profile
alignment in modeling protein families.
MSAs representing protein family core regions were taken from the curated
domains in CDD. Sequence regions outside the cores are not aligned in CDD
and are not considered in this study. Here, we use the terms domain and
protein family interchangeably. NCBI-curated domains have been organized
into hierarchical domain families. A superfamily, which indicates common
evolutionary descent, contains one or more domain families. We define
related domains with respect to CDD to be those in the same family
and unrelated domains to be those in different superfamilies, in order to
minimize false positives (FPs).Aset of 100 domains, chosen randomly from
different superfamilies, was reserved for parameter optimization (dataset
‘opt100’). Similarity between domains was estimated as the fraction identity
of their consensus sequences with pair-wise sequence alignments computed
by MUSCLE 3.6 (Edgar, 2004). The consensus sequences express only
columns in the MSA with <50% gap content, including the most conserved
columns, and hence report higher similarity values than using full length
Core regions dataset
To test alignment accuracy, we construct three benchmark datasets. The first
reference set is based on superpositions of the 3D structures that annotate
curated domains in CDD v2.14. We gather structural neighbors from the
VAST database that satisfy the default significance cutoff of P-value <
0.0001, such that folds are described by a continuous sequence region that
overlaps the sequence fragment in the domain model by 90%. To ensure
that the structure alignments involve core regions, aligned core positions are
required to comprise 80% of all structurally aligned positions and 50% of
the respective profiles. This procedure yields structure alignments for 2385
domain pairs within 91 CDD families.
A second set of structure alignments is taken from the superfamilies
set in SABmark, that is, alignments of SCOP domains with a common
evolutionary origin. CDD domains are mapped onto the SCOP domains
using RPS–BLAST (Marchler-Bauer et al., 2002). Due to time of testing,
a later version of CDD (v 2.16) was used for this benchmark set. SCOP
folds are filtered for live sequences in Entrez and at least 50% overlap
with the extent of the domain hit, resulting in structure alignments for
1627 domain pairs in 128 SCOP superfamilies. The two structural reference
sets differ in coverage across and within domain families; classification by
CDD versus SCOP; and curator-optimized versus RPS–BLAST-computed
and its sub-family contain overlapping fragments from at least one protein
sequence. The shared sequence identifies aligned columns between the two
MSAs and reflects the curator’s assertion of how the sub-family should be
mapped to its parent. Transitivity over each hierarchy extends the guide
alignment to all pair-wise comparisons in multi-domain families. Guide
alignments include 57786 domain pairs over 212 CDD families.
We describe the profile alignment algorithm with core constraint in terms of
required modifications to the canonical algorithm for local alignment (Smith
and Waterman, 1981). The problem is to align profiles A=a1···anand B=
divided into blocks. Let table H contain the maximum similarity score of
two profile segments ending in aiand bjin entry Hi,jScoring functions S(ai,
bj) to compute the similarity between profile columns aiand bjare described
in the next paragraph. To prevent gaps within blocks, the affine gap penalty
is replaced with a large negative value if the last aligned column before the
gap is not a block end. To ensure that the endpoints of the optimal alignment
fall on the N- and C-terminal of some blocks, Hi,jmay be re-initialized to
S(ai, bj) (replacing initialization to 0) if aior bjis the first column in its
respective block and traceback through Hi,jis required to terminate at that
position. Traceback may begin from the maximum Hi,jsuch that at least one
of aiand bjis the end of its respective block. These changes preserve the
O(nm) running time.
set of random alignments was simulated using all curated domains, each
aligned with 100 domains from different superfamilies. Alignment scores
were binned by the sum of lengths of the profiles. Regression curves were
the regressions were used to compute Z-score.
Much of the previous work on profile–profile alignment algorithms sought
advances through new scoring functions for comparing profile columns.
Probabilistic methods are believed to be the most effective (Mittelman et al.,
as prof_sim (Yona and Levitt, 2002), COMPASS and HHsearch. CORAL
uses a symmetrical log-odds function similar to Picasso (Heger and Holm,
2001) and COMPASS (Sadreyev and Grishin, 2003):
vectors of weighted observed frequencies of amino acids k in the respective
columns. Likewise, R is the vector of the frequency ratios of weighted
frequency for each amino acid over the background frequency of the amino
acid. Q and R are defined as for PSI-BLAST (Altschul et al., 1997; Schaffer
et al., 2001).
Surveys of scoring functions (Edgar and Sjolander, 2004; Mittelman
et al., 2003; Panchenko, 2003) have suggested that probabilistic methods
offer incremental improvements over simpler functions such as sum of
pairs (Gotoh, 1993), dot product and Pearson correlation coefficient.
Consequently, we also test the symmetrical dot product function:
In Section 3, the two methods will be denoted as CORAL LO and CORAL
A local alignment requires that the expected column score be negative and
some column score(s) be positive. To satisfy these conditions, a constant
shift value is added to each column score. To initialize the search space
for potential shift values, we computed the distributions of column scores
for correctly aligned columns in all related domains in CDD and for all
pairs of columns in a sampling of unrelated domains. A second parameter,
the gap penalty, is necessary to distinguish significant alignments. Shift
values between the means of each distribution and small gap weights were
tested systematically over combinations of both parameters. Performance
was assessed for alignment accuracy and homolog sensitivity following the
testing procedures and metrics described in Section 3. Over the opt100
dataset, performance was fairly robust over a range of parameter values.
We assigned shift values of −0.15 and 6.6 for the two scoring functions,
respectively, and gap weights of 0.1 and 0.5, respectively.
J.H.Fong et al.
To approximate the statistical significance of each alignment, we turn to the
extreme value distribution (EVD) which has been shown empirically to fit
optimal ungapped alignments of random sequences (Karlin and Altschul,
1990). It is frequently used with gapped sequences and profile alignments.
Supposing that the alignment scores follow an EVD, the E-value for every
alignment can be computed from the alignment score z and parameters λ
and µ as E=e−λ(z−µ). To determine λ and µ, normalized alignment scores
from the random alignments described above were fitted to the cumulative
density function,F(x)=exp(−exp(−λ(x−µ))). Parameters were computed
separately for each scoring function SLOand SDP. The goodness of fit is
illustrated for CORAL LO in Supplementary Figure S1.
The quality of CORAL alignments between CDD-curated domains
was evaluated against the reference alignments described in
Section 2 and compared with alignments from COMPASS 3.0,
implementation of the standard sum-of-scores optimal local
was run with default parameters and with reduced gap penalties.
To promote longer alignments, the gap open penalty was reduced
arbitrarily default from 10 to 3 and the gap extension penalty
default from 1 to 0.1. HHalign was run in local and global modes
using one domain alignment as query and the other as template.
To compute probabilities and E-values for HHalign, each HMM
was calibrated against the cal.hhm database from the download site.
For every pair of domains, a PSI-BLAST alignment was computed
between one domain and each sequence from the MSA of the other
domain, and vice versa, using the NCBI Toolkit. The sequence–
profile alignment with smallest E-value was used as the PSI-BLAST
alignment. CORALand COMPASS held a speed advantage over the
other methods, requiring than a 10th of a second for most inputs.
HHalign required 5–10s, largely because of the calibration step.
measure extent of reconstructing a reference alignment, we compute
Sdev, the ratio of the number of correctly aligned positions to
the number of aligned columns in the reference alignment. Sdev
is the same as the developer’s score of (Sauder et al., 2000). To
measure correctness, we compute Smod, the ratio of the number
of correctly aligned positions to the number of aligned columns
in the evaluated alignment where at least one of each two aligned
columns is present in the reference alignment. This is analogous to
the modeler’s score (Sauder et al., 2000), modified to include only
the profile columns that can be determined to be correct or not. The
two previous measures are summarized through a balanced score,
Sbalanced=(Sdev+Smod)/2. To more directly illustrate the trade-off
between alignment accuracy and alignment length, we estimate the
latter as Scov, the number of aligned positions divided by the length
of the shorter profile. Results from multiple structure alignments for
the same domain pair are averaged over the domain pair.
First, we analyze overall performance over CDD families and
SCOP superfamilies, both referred to as families for brevity.
An average Sbalancedfor every family is taken over its domain
pairs (Fig. 1). CORAL produced high-quality alignments for more
families than the other methods: 44% of domain families average
Fig. 1. Distribution of balanced scores from three benchmark sets: (A)
VAST structure superpositions; (B) SABmark structure alignments; and
(C) curator-inferred guide alignments. SABmark alignments are grouped
by SCOP superfamily and the others by CDD family. The balanced score
is an average of accuracy over computed alignment and completeness in
reconstructing the reference alignment.
Sbalanced≥0.8 compared with 41% by the best non-CORALmethod
SABmark benchmark and 57% versus 45% by guide alignments. In
nearly all of these families, the alignments with Sbalanced≥0.8 were
both accurate and complete. Under the three highest performing
methods (CORAL LO, CORAL DP and HHalign global), over 96%
of domain families with Sbalanced≥0.8 had both Sdev≥0.8 and
Smod≥0.8 with respect to all benchmark sets.
Comparison of Sbalancedover the domain pairs present in more
than one benchmark set reveals high consistency among the
reference alignments. For pair-wise comparison of the reference
sets, we identified domain pairs present in both benchmark sets.
Sbalancedscores for the common domain pairs, averaged over
domain families, were 0.026–0.032 lower according to the different
alignment methods in VAST alignments than the corresponding
guide alignments and 0.032–0.047 lower in SABmark alignments
than the corresponding guide alignments. Approximately 2% of
domain pairs had balanced score at least 0.01 higher by VAST
structure alignments, 30% of domain pairs had balanced score 0.01
higher for guide alignments and the remaining two-thirds of domain
pairs had negligible differences between those two references. We
hypothesize that guide alignments are more accurate because they
alignments and patterns of sequence conservation, where the latter
may overrule structure superimposition.
Nearly 19% of the domain pairs evaluated with SABmark were
assigned to different CDD superfamilies (but the same SCOP
superfamilies).Average Sbalancedscore over these pairings was less
than half that from domains in the same CDD superfamily, resulting
in a larger fraction of families with low-balanced score than from
the other reference alignments for all alignment methods (Fig. 1).
CORAL returned highest average Sbalancedscore for domains in
The higher Sbalancedscores for both CORAL methods over the
other methods suggest that the core constraint played a significant
role in improving performance for several families. For some
families, including Macro and PDZ, all members benefited from
the core constraint. Domain families that benefited the most and
the least using CORAL are listed in Supplementary Table S1.
No correlation was observed between average similarity within
families and improvement, or lack thereof, from using CORAL.
Better alignments generally came about because CORALprevented
spurious intra-block gaps and shifted blocks that were misaligned
by COMPASS and HHalign into the right positions. The families
with most negative effect from CORAL, phosphofructokinase
(PFK) and Rieske, illustrate the case where long blocks must
be split to enable a completely correct CORAL alignment.
One example is the alignment of two Rieske domains: non-
heme iron oxygenase family/nathphalene 1,2-dioxygenase sub-
family (cd03535) and small sub-unit of Arsenite oxidase family
Families such as the kinesin/myosin motor domains contain
dissimilar sub-groups such that domains within a sub-group are
aligned with much higher accuracy than domains from different
sub-groups. To account for varying difficulty, domain pairs were
grouped by sequence identity. The distribution of sequence identity
is shown in Supplementary Figure S2 with mean percent identity
ranges: 0–20%, 20–30%, 30–40% and ≥40%. Results from guide
alignments are provided in Figure 2 and referred to in the remainder
similar trends and are provided in Supplementary Figure S3. Sdev
and Smodresults within each similarity range are consistent across
most alignment methods (Fig. 2), pointing to the inherent ease or
difficulty of aligning particular domains. CORAL has highest Sdev
over all similarity ranges. Although HHalign local and COMPASS
with default arguments have higher Smodat <30% identity, CORAL
yields higher Sbalancedvalue for every similarity range. Over the
entire dataset, CORAL gives an average balanced score of 0.80
and 0.77 for the log odds and dot product functions, respectively,
compared with 0.74 for HHalign and 0.75 for COMPASS. The
shorter alignments correlate with higher alignment accuracy (Smod),
Fig. 2. Alignment accuracy in terms of the (A) Sdev; (B) Smod; and (C)
Scovmetrics based on curator-optimized (guide) reference alignments.These
metrics indicate completeness in reconstructing the reference alignment,
accuracy over the computed alignment and the local–global trade-off in the
resulting alignment, respectively.
but are less informative as they exclude more homologous regions.
alignments using a local alignment algorithm.
PSI-BLAST performance deteriorated rapidly as sequence
similarity decreases. Almost half of all domain pairs from the
same family had no significant PSI-BLAST alignment.Aligning the
consensus sequences by pairwise BLAST led to a similar outcome,
showing that these families are not as easy to align despite the
high-reported sequence identities. The default significance cut-off
for PSI-BLASTis restrictive and many domain pairs may not satisfy
the cut-off due to low-sequence similarity or short profile lengths.
Domain pairs with no PSI-BLAST results were assigned value 0
for all metrics (following the regular definitions of Sdevand Scov,
and replacing the otherwise undefined Smodterm), leading to a large
number of families with low Sbalancedscore.
J.H.Fong et al.
Fig. 3. Recognizing homologs: ROC curve plotting percentage of TP
identified before the n-th FP.
Next, we evaluated the accuracy of CORAL and its E-values at
detecting related domains. Although we do not propose to identify
homologous protein families from core regions alone, given the
evolutionary signal present in the more variable loop regions, a
scoring system helps to distinguish more similar and better-aligned
to CDD families/superfamilies, as described in Section 2. A test
set of 100 domains was taken from different superfamilies. Each
domain is aligned with all domains within the same family (with a
minimum of two related domains) and with 100 randomly selected
unrelated domains, using the alignment methods described in the
data. The distribution of sequence identity between related domains
in this test set is similar to the distribution over the entire CDD
(Supplementary Fig. S2).
Figure 3 shows performance measured as the fraction of true
relationships (true positive, TP) that score higher than the i-th
highest scoring false relationship (FP), averaged over the test set.
Scores refer to the E-values for CORAL and COMPASS and
probabilities for HHalign, which performed much better than its
E-values. To assess sensitivity, we measure the area under curve
values is computed as SE=σ?√n. ROC curves and AUC values
core regions at similar rates, and better than COMPASS. Average
ROC100and SE ranges overlapped for all CORAL and HHalign
methods and were: 0.962±0.009 for CORAL LO, 0.963±0.008
for CORALDP, 0.966±0.008 for HHalign local and 0.957±0.011
for HHalign global. There was a statistically significant difference
between the distribution of ROC100values for HHalign local, the
highest curve in Figure 3, from the closest methods HHalign global
and CORAL according to the Wilcoxon signed-rank test (P-values
0.01–0.02), but not between the CORAL methods and HHalign
global (all pair-wise P-values > 0.05).
i=1...nti, for each sample domain where tiis
the fraction of TPs before the i-th FP. Standard error over ROCn
reveal that the CORALand HHalign methods detect homologs from
The problem of aligning conserved core regions was conceived
by the need to automate domain curation and develop tools for
members that are difficult to align. Sequence similarity, for example
Alignment in protein family modeling
via characteristic motifs, can make it clear that sequence fragments
are related by common descent. More powerful tools are needed to
obtain an accurate alignment across the full domain model and to
determine domain boundaries.
In defining diverse domain families, two important and
2: to split off sub-families when applicable for increasing functional
specificity, starting with a less-diverse sub-set of sequences from
the current domain. These tasks are common to many approaches
to subfamily identification (see e.g. Brown et al., 2007), although,
here we describe steps in the CDD curation pipeline. Typically, the
higher degree of conservation in child models allows curators to
extend blocks and/or define additional blocks beyond the base core
structure of their parent. Aligning the child and parent domains
requires the selection of a representative sequence to provide the
guide alignment between a new sub-family and its parent domain.
A badly aligned representative compromises the overall alignment
of the child with respect to the parent, which may amplify noise
present in the parent and misrepresent evolutionary distance and
diversity within the superfamily. Cleaning up the child model by
itself further propagates overall error, which may be difficult to
detect. By iterating steps 1 and 2, the child alignment is refined,
its core structure may be extended or revised, and realigning the
child and parent may help to refine the core structure of the parent
When subfamilies are covered by 3D structure, structure
superposition helps to provide high-quality guide alignments.
Profile alignments augment this information and may substitute
for superpositions when structures are not known. The structure
alignment may differ markedly from the guide alignment, as in the
alignment of the eukaryotic translation factor 5A domain and the
Hex1/S1-like RNA-binding domain (Fig. 4). In this case, CORAL
validates the structural alignment and extends the aligned region. A
third major step in CDD curation is annotating domain models with
function and functional sites following the literature and analysis
of 3D structures. The alignment of related protein families helps to
confirm the locations of functional sites, which may be placed at
nearby positions in parent and child domains as shown in Figure 4
for RNA-binding sites.
Here, we showed that profile–profile alignment with well-structured
alignment constraints can achieve high-alignment accuracy and
core regions of domain families. The core constraint exploits
relationships between profile columns, prohibiting insertions or
deletions within blocks, rather than pursuing improvements through
refinement of the column scoring function. Our proposed method
is a simple interpretation of a framework in which gap penalties
vary according to local conservation, requiring only two different
gap penalties. The core constraint may be incorporated into other
alignment algorithms as well.
We benchmarked CORAL on core regions from NCBI-curated
domains in CDD. Blocks in curated domains reflect sequence
and structural conservation and approximate the structural core of
the family. However, curators may define blocks to be longer or
shorter than in structure alignments, and merge, split or delete the
blocks suggested by structure alignments. They may also introduce
Fig. 4. VAST structure, guide and CORAL alignments between the
eukaryotic translation factor 5Adomain (cd04468; eIF5A) and the Hex1/S1-
like RNA-binding domain (cd04469; S1_Hex1) are illustrated using
sequence fragments from 1X6O and 1KHI and structure superpositions. The
domains share a parent (EF- and S1-like RNA-binding domain) and 28%
identity. Aligned positions in the reference alignments are underlined. The
structure alignment is believed to be the most accurate. Misaligned regions
in the guide and CORAL alignments are colored blue. RNA-binding sites
are highlighted in yellow on both sequence and structure alignments. The
structure superposition is colored red for identical residues, purple for other
aligned residues and grey for unaligned residues.
additional blocks to record conserved features and sites outside the
structural core, such as binding sites and motifs.
CORALE-values identify 70% of all domain pairs from the same
hierarchy with E-value < 0.05 compared with 3.0% of domain
pairs from different superfamilies. Ranking scores from the same
family, as in the homology recognition test, achieves even higher
performance. In general, the CDD superfamily classification used to
the basis for remote homology in previous benchmark studies
(Marchler-Bauer et al., 2009). Nevertheless, that curated domains in
CDD are easier to classify is unsurprising, because many previous
studies aligned noisier profiles constructed by PSI-BLAST and
the hierarchical organization of CDD families suggests that many
domains have similar conserved cores.
Constructing high-quality alignments between well-defined core
regions, in contrast, benefits tremendously from the core constraint.
CORAL aligns more families with high-balanced score, produces
or HHalign across all similarity ranges, and returns higher
developer’s score for almost all groups of data. Possibly even
more importantly, by respecting block boundaries, it produces
alignments that may be easier to revise. Automated alignments
of sequences or profiles with low similarity often require manual
correction to produce optimal results. Reducing error to a small
number of block shifts simplifies manual analysis. Although the
core constraint reduces the space of possible alignment solutions,
it does not necessarily constrain the alignment to only one good
solution. Our results demonstrate that weak sequence similarity
between corresponding core regions increases errors in all methods.
Additionally, even in the more constrained setting of global
alignment, differences in profile and block lengths permit more than
one possible alignment between many blocks.
The clear shortcoming of the core constraint is that at some level
of divergence, core regions cannot be aligned correctly without
insertions or deletions, hence methods without the core constraint
are more suited to remote homolog recognition and alignment. One
solution to ameliorate shift errors is to split long blocks into shorter
units, randomly or by inspecting the block structure or preliminary
alignments of core regions. The curated domain models already
contain breaks within blocks where the sequences naturally split.
In unreported experiments, we have aligned the curated domains
overall performance. Further development of this algorithm will
allow for cases where additional blocks have been inserted into a
sub-family model relative to its parent.
CORAL will be made available to the public as an alignment
tool bundled into a future release of the NCBI Cn3D/CDTree
software. This user-friendly implementation will provide fast and
accurate alignment of core regions, along with access to protein
family alignments from CDD. While we only tested alignments
between pre-computed protein family models, core regions may
be inferred from the continuous regions of any protein family
alignment. However, the effective use of CORAL requires high
overlap between the conserved regions of two families, for example,
in the case of a common structural core, and additional processing
may be needed to identify putative conserved core regions. The
core constraint may also be incorporated into profile alignment
algorithms with more sophisticated scoring methods to improve on
both CORAL and the original method for aligning conserved cores.
We thankAnna Panchenko and John Spouge for helpful discussions.
Funding: Intramural Research Program of the National Institutes of
Health, National Library of Medicine.
Conflict of Interest: none declared.
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