METHODOLOGY ARTICLE Open Access
FACT: Functional annotation transfer between
proteins with similar feature architectures
Tina Koestler1,2,3,4
, Arndt von Haeseler1,2,3,4
, Ingo Ebersberger1,2,3,4*
Abstract
Background: The increasing number of sequenced genomes provides the basis for exploring the genetic and
functional diversity within the tree of life. Only a tiny fraction of the encoded proteins undergoes a thorough
experimental characterization. For the remainder, bioinformatics annotation tools are the only means to infer their
function. Exploiting significant sequence similarities to already characterized proteins, commonly taken as evidence
for homology, is the prevalent method to deduce functional equivalence. Such methods fail when homologs are
too diverged, or when they have assumed a different function. Finally, due to convergent evolution, functional
equivalence is not necessarily linked to common ancestry. Therefore complementary approaches are required to
identify functional equivalents.
Results: We present the Feature Architecture Comparison Tool http://www.cibiv.at/FACT to search for functionally
equivalent proteins. FACT uses the similarity between feature architectures of two proteins, i.e., the arrangements
of functional domains, secondary structure elements and compositional properties, as a proxy for their functional
equivalence. A scoring function measures feature architecture similarities, which enables searching for functional
equivalents in entire proteomes. Our evaluation of 9,570 EC classified enzymes revealed that FACT, using the full
feature, set outperformed the existing architecture-based approaches by identifying significantly more functional
equivalents as highest scoring proteins. We show that FACT can identify functional equivalents that share no
significant sequence similarity. However, when the highest scoring protein of FACT is also the protein with the
highest local sequence similarity, it is in 99% of the cases functionally equivalent to the query. We demonstrate the
versatility of FACT by identifying a missing link in the yeast glutathione metabolism and also by searching for the
human GolgA5 equivalent in Trypanosoma brucei.
Conclusions: FACT facilitates a quick and sensitive search for functionally equivalent proteins in entire proteomes.
FACT is complementary to approaches using sequence similarity to identify proteins with the same function. Thus,
FACT is particularly useful when functional equivalents need to be identified in evolutionarily distant species, or
when functional equivalents are not homologous. The most reliable annotation transfers, however, are achieved
when feature architecture similarity and sequence similarity are jointly taken into account.
Background
The sequencing of entire genomes has become a routine
task in molecular biology. To date, about 240 fully
sequenced eukaryotic genomes comprising more than
3.7 Million protein coding sequences are available in the
public domain [1]. Only a small fraction of these species
are model organisms with considerably well characterized
protein functions. Most of the remaining species
are either of commercial or medical interest, qualify for
new model organisms, or hold key positions required
for the understanding of organismal evolution. The benefit
of a newly sequenced organism essentially depends
on the extent to which its data is integrated into existing
knowledge about function and evolutionary relationships
of genes in other species. A thorough experimental
characterization of all proteins is not feasible. Therefore,
comprehensive bioinformatics approaches are needed to
reliably identify functionally equivalent proteins across
species. Two roads are usually followed to accomplish
this task.
* Correspondence: ingo.ebersberger@univie.ac.at
1
Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz
Laboratories, Dr. Bohrgasse 9, A-1030 Vienna, Austria
Full list of author information is available at the end of the article
Koestler et al. BMC Bioinformatics 2010, 11:417
http://www.biomedcentral.com/1471-2105/11/417
© 2010 Koestler et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
The first and more common approach searches for
proteins with a significant sequence similarity, which is
commonly taken as evidence for their common ancestry.
For example, a protein with unknown function can be
used as query to search for similar sequences in annotated
protein databases, e.g., with BLAST [2] or, for
more sensitive searches, using machine learning algorithms,
like PsiBLAST [2] or support vector machines
[3-5]. The functional annotations of the best hits serve
then as tentative annotations for the query, e.g., [6,7].
Clearly, one limitation is inherent in this approach:
Functional equivalence is not tied to a significant
sequence similarity. This can have several reasons: First,
a query may not obtain a significant hit in a similarity
search since the homologous proteins with the same
function are too diverged, or are of low complexity. Second,
homologs may be identified via sequence similarity
but they have assumed diffrent functions [8,9]. For
example, in the case of enzymes about 60% of sequence
identity between homologous proteins is required to
reliably infer functional equivalence [10,11]. Thus, a
functional annotation transfer between homologs can be
wrong. If such an error remains undetected, it can
spread through databases [12]. Third, it has been shown
that proteins with the same function are not always
homologous, but rather are a result of convergent evolution
[13]. In such instances sequence similarity based
searches for functional equivalents produce no results.
In summary, functional equivalence is not synonymous
with homology. The second approach to identify functional
equivalents does not rely on homology inference
by means of pair-wise sequence similarity but rather
considers other measures of protein similarity. Amino
acid sequences can be annotated with a plethora of features,
capturing different properties of the protein.
Among others, these are functional domains, secondary
structure elements and compositional properties.
The aggregate of all features in a protein constitutes
its feature architecture, and it is supposed that this feature
architecture allows conclusions about the function
of a protein. A number of studies have shown the
applicability of such a feature based approach, e.g.,
[14,15]. The possibility to deduce protein function from
the feature architecture suggests that feature architecture
similarity can be used to identify proteins sharing a
similar function. For example, InParanoid displays the
Pfam [16] domain annotation of homologous proteins
[17]. Thus, we can quickly assess if homologs can be
functional equivalents. In the same way, ProteinArchitect
[18] finds similar proteins to a query sequence and
displays the feature architecture of the hits. However,
these tools provide the feature annotation only as an
accessory information. The search for similar proteins in
the first place is still performed on the amino acid
sequence level. The necessity to include information
about the feature architecture into the search for functional
equivalents was emphasized by Forslund et al.
2008 [19]. They showed that roughly 12% of the feature
architectures in 96 eukaryotic proteomes evolved more
than once independently. Hence, the corresponding proteins
are functionally similar although they are not
homologous.
Despite its potential for identifying functionally
equivalent proteins, only few strategies exploit the feature
architecture for similarity searches [20-22]. Lin et
al. [20] were the first to measure the similarity between
feature architectures using a weighted sum of three
indices. The first index measures the ratio of shared features
to the total amount of features. The second index
assesses the feature duplication similarity, and the third,
the Goodman-Kruskal index, measures to what extent
the same feature pairs occur in two proteins. A detailed
description of the Lin score is given in the implementation
section. Lee and Lee 2009 [22] additionally introduced
a weighting scheme that reduces the influence of
promiscuous Pfam domains [23]. Notably, all
approaches share the same limitations. Most importantly,
feature architectures are constructed only from
Pfam domains. Thus, other features such as transmembrane
regions or coiled coil domains indicative of protein
function are ignored. Furthermore, the positional
information of shared features in the compared proteins
is not taken into account. Eventually, a systematic evaluation
to what extent feature architecture similarity is
helpful in detecting functional equivalents is also missing.
Lin et al. 2006 [20] and Lee and Lee 2009 [22] evaluated
their approaches only for their capability of
detecting homologous proteins. Thus, the search for
functional equivalents using feature architecture similarity
is still in its infancy.
Here, we present FACT a comprehensive method for
searching for functionally equivalent proteins using the
criterion of feature architecture similarity. FACT considers
a broad spectrum of features (functional domains,
secondary structure elements, and compositional properties)
to determine the feature architecture of a protein.
Moreover, the positions of the features in a protein
sequence are taken into account. FACT can be used to
search for functional equivalents in entire proteomes
and the credibility of the best hit is assessed by a pvalue.
This makes an automated large scale search for
functional equivalents possible. A graphical interface,
the feature dotplot, complements the automated similarity
search and facilitates a visual comparison of two feature
architectures. We evaluate the fidelity of FACT
using a collection of EC classified enzymes and demonstrate
FACT’s applicability for identifying functional
equivalents. A comparison to the performance of
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existing approaches to infer functional equivalence from
feature architecture similarity, as e.g., described in
[20,22], on the same set of enzymes is used to assess the
improvement of FACT. Finally, we compare for the first
time the usability of two protein similarity measures,
sequence similarity and feature architecture similarity,
for identifying functional equivalents, and we explore
their respective strengths and weaknesses.
Implementation
As a first step, FACT annotates the query and each protein
in the search set with a broad variety of features
(Figure 1A), i.e., functional domains (Pfam domains,
SMART domains [24], transmembrane regions, signal
peptides), secondary structure elements (helix, strand,
coiled coils), and compositional properties (low complexity
regions, sequence composition). A pipeline of
several feature prediction programs serves this purpose.
The underlying feature set F is, therefore, determined
by the collection of prediction programs. The feature
architecture of a protein is then the arrangement of
instances of features in F (Figure 1B).
Measuring the similarity of feature architectures
To identify proteins with a similar feature architecture
to a query protein Q, we measure the pairwise similarity
between Q and every protein P in a proteome. We
implemented a modified version of the score from Lin
et al. 2006 [20] and introduce the FACT score.
Modified Lin score (MLS)
Lin et al. [20] score the similarity of two Pfam based feature
architectures by combining the Jaccard index, a
domain duplication similarity index, and the GoodmanKruskal
index with relative weights 0.36, 0.63, and 0.01,
respectively. Calculating the Goodman-Kruskal index
requires the order of Pfam domains along the sequence.
Our feature set F contains a plethora of additional features
that can overlap in the feature architecture (c.f. Figure
1B). In such instances, it is unclear how to assess the
feature order. However, given its low relative weight of
0.01, the contribution of the Goodman-Kruskal index to
the total score is negligible. Thus, we ignored this index
in our implementation and adapted the weights of the
two other indices accordingly. We calculate the MLS as
L P Q
N PQ
N P NQ N PQ
N N N
i
N N
i
P
i
Q
m
P Q
( , ) . *
. *exp( | | /
=
+ −
+
− −
=
+
∑
0 365
0 635
1
aax),
(1)
where NPQ
is the number of shared features between
protein P and the query protein Q. NP
and NQ
are the
number of different features in P and Q, respectively.
Ni
P and Ni
Q count the instances of feature i in P and
Q, respectively and
N N N
i
N N
i
P
i
Q
P Q
max max= ( )
=
+
∑1
, . (2)
One drawback of the MLS is that it does not include
information about the position of individual features in the
proteins. Therefore, we introduce a new scoring function.
FACT score
The FACT score computes the feature architecture similarity
between proteins as the weighted sum of three
scores considering (i) the number of instances for all
shared features, (ii) Pfam clan annotations, and (iii) the
positions of shared features in the proteins. We describe
the three building blocks of the FACT score in the following
paragraphs.
Feature multiplicity similarity (MS) The MS assesses
to what extent the numbers of instances for a shared
feature agree between two architectures. For each shared
feature i, we compute the product of its number of
instances in P Ni
P
( ) and Q Ni
Q
( ), and normalize this
number by the theoretically maximal value
max( , )N Ni
P
i
Q 2 . The MS is then the weighted sum over
all shared features.
MS P Q
Ni
P Ni
Q
Ni
P Ni
Qi
i
N PQ
( , ) *
*
( , )
,=
=
∑
1
2max
(3)
where ωi > 0 is the weight for feature i. We use two
weighting functions. First, ωi = 1/NQ
where i = 1, ...,
NPQ
. This corresponds to an equal weighting of all features
in Q. The resulting score is called MSuni. Second,
we include the frequency of a feature i from Q in P into
the weighting. To this end, Ni
P counts how often feature
i from Q is observed in P. The corresponding
weight is then
i
Ni
P
j
NQ
N j
P
=
=Σ 1
, (4)
where i = (1, ..., NQ
). This ensures that
Σi
N
i
Q
= =1 1 .
It is now straightforward to extend this weighting to a
set of proteins {P1, P2, ..., Pl}, e.g., a search proteome.
We calculate the weight as
i
k
l
Ni
Pk
k
l
j
NQ
N j
Pk
=
=
= =
Σ
Σ Σ
1
1 1
. (5)
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Figure 1 Overview of FACT. (A) The amino acid sequence of query protein Q serves as input for a collection of prediction programs, which
annotate Q with features from F. (B) The assembly of instances from F constitute the feature architecture of a protein. (C) The FACT score
captures the similarity between two feature architectures by a combination of the Feature multiplicity similarity (MS), the Pfam clan similarity (CS),
and the Feature positional similarity (PS). The score is calculated between Q and every protein in a pre-annotated search proteome resulting in a
list where the proteins in the search proteome are ranked in decreasing order according to their FACT score. (D) From the score list any protein
P can be extracted and its feature architectures similarity to Q can be visualized in the feature dotplot.
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We refer to this score as MSst. In the MSst, feature
architectures sharing features that are rare in the search
proteome receive a higher score than those sharing frequent
features. This reflects the intuition that shared
rare features are more likely to point towards a similar
function than shared frequent features.
Pfam Clan Similarity (CS) Pfam groups similar
domains into clans [16]. For example, the Pfam clan
RNase_H (CL0219) consists of 25 domains with a tertiary
structure similar to that of Ribonuclease H. This
structural similarity implies similarity in the function of
the clan members. The CS score takes into account the
co-occurrence of different Pfam domains in a clan. It is
calculated analogously to the MSuni score.
CS P Q
C
Ci
P Ci
Q
Ci
P Ci
QQ
i
cPQ
( , )
*
( , )
,=
=
∑
1
2
1 max
(6)
where CQ
is the number of different Pfam clans in Q,
CPQ
is the number of shared Pfam clans between P and
Q, and Ci
P are the numbers of instances of clan i in P
and Q, respectively.
Feature positional similarity (PS) The PS measures
the distance between the relative positions a shared feature
occupies in the compared proteins. For every
instance of a shared feature in P and Q, we first determine
the relative position within P and Q. Subsequently,
we identify for each instance in Q the instance in protein
P having minimal distance. One minus the minimal
distance between two feature instances yields a similarity.
We calculate PS as following
PS P Q i
Ni
Q
q p
i
N
l N
j l
j
NPQ
i
P
i
Q
( , ) ( min | |),= − −
=
≤ ≤
=
∑ ∑
1
1
1
1 (7)
where the relative position qj of the jth
instance of feature
i in protein Q is the center position of this instance
divided by the sequence length. The positions pl of the
feature instances in protein P are calculated accordingly.
The use of relative positions ensures that shared features
located at the C-terminus in both proteins have a small
distance even if the protein lengths are different. The
weights ωi of the individual features correspond to those
of the MSst.
The FACT score is a weighted linear combination of
the Feature multiplicity similarity (MSst), the Pfam clan
similarity (CS), and the Feature position similarity (PS)
(Figure 1C).
FACT MS CS PSst= ∗ + ∗ + ∗ ∈[ ]   0 1, , (8)
where a + b + g = 1, and a, b, g ≥ 0.
Score statistics
Using the scoring functions introduced in the previous
section, we calculate the feature architecture similarity
scores between a query protein and every protein in a
search proteome. From the resulting distribution of
scores, we assess the extent to which the top scoring
protein stands out from the lower ranking proteins (Figure
1D). For this purpose, we fit a beta distribution [25]
to the score histogram. We have chosen the beta distribution
for two reasons. First, it can assume different
shapes. This fits well with histograms, even when different
scoring functions are used (Figure 2). Second, it is
defined in the range from 0 to 1, which is the exact
range of the scores. We estimate the two shape parameters
of the beta distribution from the mean, and the
variance from all scores by the method-of-moments
[25]. The p-value for a score x is then calculated as one
minus the cumulative distribution function of the beta
distribution of x. The smaller the p-value is, the more
pronounced is the feature architecture similarity
between the query and the highest scoring protein compared
to that of the lower ranking proteins.
Feature Dotplot
For a visual inspection of individual query hit pairs, we
have developed the feature dotplot (FDP, Figure 1D),
which extends the idea of a classical dotplot to the feature
level. The FDP projects the features of two proteins
along the x-and y-axis, respectively. A feature occurring
in both proteins is represented by a diagonal line in the
dotplot, where the slope of the line indicates the length
ratio of the features in the proteins. Different features
are represented by different colors. The standard amino
acid dotplot is embedded into the FDP as well.
FACT webpage
FACT is provided online on the webpage http://www.
cibiv.at/FACT. The user can search for functional
equivalents to a query protein in entire proteomes. Currently,
the collection consists of 26 eukaryotic species
(13 animals, 7 fungi, 3 plants, and 3 protists), but
further species will be added. For every query protein
FACT determines the feature architecture. Then the
FACT, MSuni, MSst and the MLS scores between the
query protein and all proteins in a search proteome are
computed. For each scoring function the 100 highest
scoring proteins are listed and a histogram of all scores
is displayed. Additionally, the p-values for the highest
scoring protein are shown. The FDP between the query
protein and any protein from the score list can be
viewed. The FDP links Pfam and SMART domains to
the corresponding web pages. Furthermore, possibilities
for displaying or hiding specific features, changing the
word size for the amino acid dotplot and for exporting
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the feature dotplot are provided. Finally, a BLAST
search against the search proteome is performed and
the best three hits are listed. A FACT search conducted
on this webpage takes currently about 1 to 5 minutes
depending on the size of the search proteome. As an
alternative to the proteome wide search, the FDP can be
used to compare two user-defined proteins. The features
of both sequences are annotated automatically and displayed
in the dotplot.
Results
FACT has been developed for identifying functionally
equivalent proteins. To assess the applicability of our
program we require that the tested proteins have the
exact function assigned. To the best of our knowledge,
only the proteins annotated by the Enzyme Commission
(EC) satisfy this condition. The EC provides a hierarchical
classification of the reaction catalyzed by an enzyme.
The code consists of four numbers separated by dots.
The first number determines the main catalyzed reaction
(1 = Oxidoreductases, 2 = Transferase, 3 = Hydrolases,
4 = Lyases, 5 = Isomerases, 6 = Ligases), while the
last number provides the most specific information
about the catalyzed reaction. If two enzymes share the
same EC number, they catalyze the same reaction and
are therefore functional equivalents. Thus, the EC classifies
enzymes according to their function and not according
to their evolutionary relationships (c.f., [13]). We
collected EC annotated proteins from human, fly, worm,
yeast, and arabidopsis and filtered the dataset such that
each EC number is represented at least twice. The final
test set is comprised of 9,570 proteins. The average and
median numbers of proteins with the same EC number
are 10 and 4, respectively.
Comparison of different scoring functions
For our evaluation, each protein from the test set served
as a query for FACT. The similarity scores between the
query protein and the remaining 9,569 proteins from
the test set were then calculated. Subsequently, we compared
the EC number of the highest scoring protein(s)
to that of the query. If one highest scoring protein has
the same EC number as the query, the proteins are
functional equivalents. The fidelity of a scoring function
is then the percentage of searches where a functional
equivalent gets the best score. Table 1 shows the fidelities
for the different scoring functions. For the FACT
score we chose a, b and g in the ratio 3:1:1 (cf. equation
8). The MLS and MSuni display fidelities of around 80%,
thus in 20% of the 9,570 searches a protein that is not
Figure 2 Fit of the beta distribution to the score histograms. Shown are typical score histograms from two FACT searches in the T. brucei
proteome using the scoring function (A) MSuni, and (B) MSst. Despite the different shapes of the histograms, the beta distribution displays in
both cases a good fit to the data.
Table 1 Fidelity of FACT using different scoring
functions.
scoring function # prot (%)
MLS (Eq. 1) 7,685 (80.30)
MSuni (Eq. 3) 7,712 (80.59)
MSst (Eq. 3, 5) 7,908 (82.63)
FACT (Eq. 8) 8,017 (83.77)
’# prot (%)’ denotes the number and percentage of correctly identified
functional equivalents in the test set of 9,570 proteins.
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functionally equivalent to the query obtains the highest
score. Weighting the individual features according to
their frequency in the test set (MSst) increases the fidelity
to 83%. The best result was obtained with the FACT
score which also takes clan similarity and positional
information into account. In 8,017 out of 9,570 cases
(84%), a functional equivalent to the query obtained the
highest score.
When we analyzed the fidelity with respect to the
main reaction catalyzed (first digit of the EC number), a
functional equivalent was identified for 9,018 query proteins
(94%; FACT score).
Relevance of features
In addition to Pfam and SMART domains, the underlying
feature set F of FACT includes a variety of other
protein features, e.g., secondary structure elements and
compositional properties. We next assessed the relevance
of including these features. We compared the
fidelity of the functional equivalent search using Pfam
domains only to the fidelity based on the full feature set.
The median number of proteins having the same best
score is 9-13 (depending on the scoring function) for
the Pfam only set. This number decreases to 1 for the
full feature set. Thus, considering a broad variety of features
leads to a better discrimination in the assessment
of feature architecture similarity. In contrast, searches
using only Pfam domains frequently end up with many
equally best scoring proteins representing different EC
numbers. For further evaluation, we consider a hit protein
only then as an identified functional equivalent
when its EC number matches that of the query, and
additionally when it is uniquely top ranked in the score
list. Table 2 shows the results of this analysis. The fidelities
for the Pfam only set range, depending on the scoring
function, between 6 and 9%. Using the full feature
set leads to a drastic increase of the fidelity to values
between 58 and 74%.
p-value for FACT hits
For each highest scoring protein a p-value is calculated.
We determined the relation between p-value and the
fidelity of FACT using the FACT score. With a
decreasing p-value, the fidelity increases to a maximum
of 98% at a p-value smaller than 10-11
(see Additional
file 1, Figure S1). Considering only those functional
equivalents as identified that are uniquely top ranked,
the fidelity increases up to 85% at a p-value below 10-9
.
However, as expected, the increased fidelity comes at
the cost of the coverage. For example, of the 9,570
searches only 1,558 have a highest scoring protein with
a p-value smaller than 10-9
(see Additional file 1, Table
S1). Our analysis shows that an annotation transfer
between the query hit pair becomes more reliable when
the p-value is small. Thus, we conclude that the choice
of the beta distribution leads to sensible results.
Feature architecture similarity vs. sequence similarity as a
proxy for functional equivalence
With FACT we provide a comprehensive tool to search
for functional equivalents using feature architecture
similarity. We now compare FACT to the alternative
approach that identifies functional equivalents via a significant
sequence similarity, e.g., using BLAST [6,7].
Therefore, we run both methods on the test set. To ease
the comparison between the two results, we again
required a correctly identified functional equivalent to
be uniquely top ranked. Figure 3 breaks down the
results from BLAST and FACT (FACT score). BLAST
identified 6,935 (72.5%) functional equivalents compared
to 7,091 (74.1%) for FACT. In 5,805 (60.7%) searches
both approaches obtained a functional equivalent as
highest scoring protein. Moreover, in 4,017 (42%)
searches the highest scoring proteins were even identical.
1,286 (13.4%) functional equivalents were detected
exclusively by FACT, whereas 1,130 (11.8%) were
detected only by BLAST. Although FACT performs
Table 2 Fidelity of FACT depending on the feature set.
Pfam domains all features
scoring function # prot (%) # prot (%)
MLS 891 (9.31) 5,618 (59.70)
MSuni 572 (5.98) 5,592 (58.43)
MSst 594 (6.21) 5,792 (60.52)
FACT - 7,091 (74.10)
’# prot (%)’ denotes the number of top ranked functional equivalents with a
unique highest score. Since the FACT scoring considers clan information it was
not used for the calculation with only Pfam domains.
Figure 3 Venn diagram contrasting the performance of FACT
(FACT score) and BLAST on the test set. Given are the the
numbers of uniquely top ranking proteins having the same EC
number as the query. For 1,286 (FACT) and 1,130 (BLAST) queries,
respectively, only one program identified a functional equivalent.
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slightly better than BLAST, the large number of functional
equivalents found only by BLAST indicates that
both approaches are complementary. This conjecture is
further corroborated by the following observation:
When FACT and BLAST detect the same protein as
best hit, the query hit pair is in 99% of the cases functionally
equivalent.
FACT outperforms BLAST in situations where
sequence similarity between functional equivalents is
low. When the E-value exceeds one, the best BLAST hit
is only in 1% a functional equivalent. For the same
query proteins FACT still achieves a fidelity of 31%
(Additional file 1, Figure S2). For E-values ≥10-20
the
fidelity of BLAST increases to 39%, but it is still higher
for FACT (46%).
To further explore the complementarity of both
approaches we conducted a more detailed analysis. For
any query protein in our test set, BLAST and FACT
each identified a top scoring protein with an associated
E-value and p-value, respectively. First we showed that
E-value and p-value are not correlated (Pearson correlation
cofficient: 0.09). Thus, a query obtaining a BLAST
hit with a small E-value does not imply a FACT hit with
a small p-value, and vice versa. Second, we binned the
query proteins according to their E-value/p-value combination.
For each combination, we counted the number
of query proteins that fall into the bin. Then for each
bin we counted how often BLAST and FACT identified
a functional equivalent. These numbers are represented
in the matrix shown in Figure 4. This matrix gives a
guideline under which E-value/p-value combination
either BLAST or FACT is more likely to find a functional
equivalent. For query proteins obtaining poor
BLAST hits (E-value > 0.1), the FACT predictions are
more credible. A similar picture emerges for queries
having a BLAST hit with a reported E-value of zero.
Once the p-value exceeds 10-3
, FACT always identifies
more functional equivalents than BLAST. Finally, we
note that PsiBLAST is more sensitive than BLAST in
detecting even weak sequence similarities that may be
indicative of a similar function. We therefore compared
FACT also to PsiBLAST. This confirmed our findings
from the FACT-BLAST comparison (see Additional file
1, Figures S3 and S4).
Example applications of FACT
To illustrate the versatility of FACT in searching for
functional equivalents we discuss two examples. The
general procedure of a FACT search is summarized in
Figure 1.
Missing link in the glutathione metabolic pathway
A common task in comparative genomics is the identification
of proteins that are involved in known metabolic
pathways in different species. As of today, the
evolutionary relationships between proteins are usually
used for this purpose, e.g., [26]. In some cases however,
orthologs to individual proteins cannot be identified.
Consequently, the question is raised of whether the corresponding
functional equivalents are not present in the
respective species or whether sequence similarity based
searches cannot detect them. The glutathione metabolic
pathway in the KEGG database [26] constitutes one
illustrative example. It is one of the central detoxification
pathways in animals and fungi. An ortholog to the
human glutathione S-transferase (EC number 2.5.1.18), a
central enzyme in this pathway, is not annotated in the
yeast genome. However, orthologs to the human proteins
flanking the glutathione S-transferase in the pathway
are present (Figure 5).
A BLAST search using the human glutathione S-transferase
protein as query revealed no significant hits in the
yeast proteome. The best BLAST hit (YNL286W; Evalue
= 0.51) has no feature in common with the
human query protein except a-helices and b-sheets.
Instead, it contains two RNA recognition motifs
(RRM_1). Similarly, the best PsiBLAST hit (YCL009C;
E-value = 1.3) has no feature in common with the
human query protein except a-helices and b-sheets.
Next, we performed a FACT search in the yeast proteome,
again with the human enzyme as query. This
revealed the same best hit for all scoring functions
(YLL060C; FACT score: p-value = 3 × 10-6
). Thus, from
the corresponding E-value/p-value entry in Figure 4
(>10-1
/]10-6
, 10-5
]), there is a 50% chance of having
detected a functional equivalent. We next used the FDP
of the FACT hit and the query protein to validate the
candidate (Figure 6). Both proteins have the N-terminal
and the C-terminal glutathione S-transferase (GST)
domains and share a predicted transmembrane region.
Therefore, we conclude that the two proteins are functional
equivalents. This has indeed been confirmed,
since both proteins have been annotated with the same
EC number [27]. Thus, FACT helps to identify candidate
proteins that may close gaps in biochemical
pathways.
Functional equivalents for GolgA5
In our second example we focussed on a structural protein,
GolgA5, which is important for assembling and
maintaining the structure of the human golgi apparatus
[28,29]. Almost the entire protein is made up of coiled
coils. This structure is formed by low complexity repeat
units consisting of hydrophobic and polar residues. Consequently,
many different sequences can assume a coiled
coil structure. Thus, a sequence similarity based search
for functional equivalents in very distantly related species
is likely to be not successful. We performed a
FACT search with the human GolgA5 in Trypanosoma
brucei. The highest scoring protein agrees only between
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the MSst and the FACT score (Tb11.02.5040), while two
different proteins were identified by the MSuni
(Tb11.02.4670) and the MLS (Tb927.5.1900). For that
reason, all top ranked hits and the best BLAST hit
(Tb11.52.0008) and best PsiBLAST hit (Tb927.7.3330)
were analyzed with the FDP (see Additional file 1,
Figure S5-S8. The FDP of the PsiBLAST hit is not
shown since this protein is 4,334 aa in length.) Since the
function of GolgA5 requires its anchoring in the plasma
membrane, we curated the results according to the presence
of a transmembrane region. Among all hits,
Tb11.02.4670 (MSuni) is the only protein that shares a
Figure 4 Contrast of BLAST and FACT (FACT score) for different E-value/p-value combinations. The matrix bins the 9,570 proteins
according to the E-value and the p-value of the best hit when used as query for BLAST and FACT, respectively. The total number of proteins for
a E-value/p-value combination is given by the bottom number in the corresponding cell. The two further numbers in a cell give the number of
searches FACT (top) and BLAST (middle) had a functional equivalent as top scoring protein. The count number for the better performing tool is
given in bold face. Yellow cells show E-value/p-value combinations where FACT identified more functional equivalents than BLAST, whereas the
blue cells indicate a higher fidelity of BLAST. Grey cells mark ties.
Figure 5 Section of the KEGG glutathione metabolic pathway (ko00480). Grey filled boxes represent proteins of the human pathway for
which KEGG orthologs exist in S. cerevisiae. An ortholog to the human glutathione S-transferase (EC 2.5.1.18), a central component of this
pathway, could not be identified in yeast.
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C-terminal transmembrane region with the human
GolgA5. Thus, we consider it to be the most promising
candidate for the GolgA5 functional equivalent in
T. brucei. Notably, it was recently shown that this protein
exerts the same function in T. brucei as GolgA5
does in humans [30].
Discussion
Here we present FACT, a tool for searching for functionally
equivalent proteins. FACT computes the pairwise
similarity between feature architectures and
identifies for a query protein the highest scoring hit in
an entire proteome. Evaluating the performance of
FACT on EC classified enzymes reveals a fidelity of 84%.
How to measure the similarity between feature architectures
still remains an open question. So far, all suggestions
are ad-hoc solutions to the scoring problem.
For example, the Lin score [20] assesses the similarity
between two proteins from their features in common
and also considers the set difference. Thus, features that
are not shared between two proteins reduce the score.
This scoring appears reasonable when feature architectures
consist only of functional domains, e.g., Pfam
domains. In such cases, the presence of an extra feature
in one protein is likely to also reflect a functional difference
between the compared proteins. However, in our
study we used a comprehensive feature set, where some
features lack an obvious connection to function. Therefore,
we introduce a new score that considers only
shared features. Our evaluation on a set of EC classified
enzymes reveals that the fidelity in identifying functional
equivalents does not heavily depend on whether or not
the feature set difference between two proteins is taken
into account. Both scoring functions, the MLS and
MSuni perform equally well. Their conceptual difference,
however, becomes relevant in individual cases as shown
by our GolgA5 example application. The best scoring
protein according to the MLS shares 4 features with the
query and has one extra feature (c.f. Additional file 1,
Figure S3). In contrast, MSuni identifies a highest scoring
protein that shares 5 features with the query but has 4
extra features (c.f. Additional file 1, Figure S4).
The idea of giving individual features different weights
has been presented before. Lee and Lee 2009 [22]
weight a Pfam domain depending on its frequency in
the RefSeq database [31] and on the diversity of its
flanking Pfam domains. Note that the latter criterion is
not straightforward to implement when features can
fully overlap, and hence, feature order cannot be determined.
In the MSst, we weight a feature according to its
inverse frequency in the search proteome. This weighting
scheme can be applied to any feature, and takes into
account that feature frequencies can vary between
search proteomes. The comparison of MSuni and MSst
reveals that the introduction of weighting increases the
fidelity by 2%. Unfortunately, comparing the effect of
our weighting to that of Lee and Lee 2009 [22] is
impossible, since in their evaluation the scoring functions
differed not only in the weighting but also in the
way shared domains are scored.
Among all scoring functions, the FACT score performs
best (Table 1 and 2). This is the consequence of
including clan similarity and positional similarity. We
compute the FACT score by combining the scoring
functions MSst, CS, and PS in a ratio of 3:1:1. We consider
the number of shared features and their number of
instances to be the most important parameters in determining
the similarity between feature architectures. The
clan annotation as well as the position of features are
supplementary information that only have a moderate
influence on the final score. Note that we deliberately
did not optimize the weight parameters a, b, and g with
respect to the fidelity on the EC based functional annotation.
Enzymes cover only a fraction of the diversity of
protein functions. We wanted to avoid a bias towards
this particular class of proteins, which could interfere
with the general applicability of FACT [32].
In contrast to existing tools that use Pfam domains for
identifying functionally similar proteins [20,22], FACT
recruits a diverse set of features for building the feature
architectures. Our evaluation highlights the significance
of using a comprehensive feature set. When considering
only Pfam domains, the median number of equally best
scoring proteins is 9-13, depending on the scoring function.
The most extreme case comprises the 589 enzymes
lacking any Pfam domain. When these proteins are used
Figure 6 Feature dotplot of the human glutathione Stransferase
and the best FACT hit in yeast. The two proteins
share the Pfam domains GST_N (PF02798) and GST_C (PF00043), as
well as a transmembrane domain. A signal peptide (signalP) is
present only in the human protein. For better readability a helix
and b sheet annotations are not shown.
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as query, all proteins in the search proteome obtain the
same score. However, the median number of enzymes
with the same EC number as the query is only 3. Consequently,
in the vast majority of searches more than one
EC number is represented by the top ranked proteins.
The search result is therefore ambiguous. To facilitate a
meaningful assessment of the fidelity, we required a correctly
identified functional equivalent to be uniquely top
ranked. This reveals a maximal fidelity of 9% (Table 2).
In contrast, when we use the FACT feature set, the
median number of equally best scoring proteins reduces
to one. This shows that the similarity score becomes
more discriminative when more features are considered.
As a consequence, the fidelity raises to 74% (FACT
score). Notably, for the proteins without Pfam domains
a correct functional equivalent was still identified in 158
cases.
There is still room for improvement regarding the
search for functional equivalents. So far, all approaches
are based on ad-hoc solutions for measuring the similarity
between feature architectures since modeling their
evolution is still an open problem. Moreover, the function
of a protein essentially depends on its tertiary
structure. However, tertiary structure elements are not
yet part of the feature set. Both the integration of evolutionary
models and of complex features is likely to result
in more sensible similarity measures.
Feature architecture similarity based approaches identify
functional equivalents. This supposedly complements
sequence similarity based approaches
represented, e.g., by BLAST or PsiBLAST. Here we have
compared the fidelity of FACT to that of BLAST. A
substantial fraction of functional equivalents were top
ranked exclusively by FACT. This includes the cases
where sequence similarity was too low to result in a significant
BLAST hit, but FACT still detected functional
equivalents. Finally, we observed no linear correlation
between the E-value of the best BLAST hit and the pvalue
of the best FACT hit for a given query. In summary,
these results confirm the complementarity of feature
architecture similarity based approaches and
sequence similarity based approaches in the search for
functional equivalents. This finding is independent of
whether we used BLAST or PsiBLAST. The complementarity
is further corroborated by those searches
where FACT and BLAST identify the same best hit. In
such instances, the fidelity increases to 99%. Thus, a
joint application of a feature architecture measure and a
sequence similarity measure allows for highly reliable
automated functional annotation transfers. However,
this increase of the fidelity comes at the cost of detecting
only 42% of the present functional equivalents in
our test data. In cases where the two approaches disagree,
we need to decide which of the hits is more likely
to be a functional equivalent. To facilitate this decision,
we have compared the fidelities of BLAST/PsiBLAST
and FACT depending on the E-value and p-value of the
highest scoring protein for a given query (c.f. Figure 4,
Additional file 1, Figure S4). Notably, for searches where
both methods obtained a good hit, i.e., small E-value
and small p-value, respectively, FACT finds a functional
equivalent more often than the other program. However,
in most cases, a decision of whether a FACT hit that is
not confirmed by BLAST, or vice versa, is a functional
equivalent will require manual curation. We have presented
two examples where we searched for functional
equivalents to the human glutathion S-transferase in
yeast, and to the human GolgA5 in T. brucei. These
examples showed that the feature dotplot is a versatile
tool to curate results from FACT searches. The feature
dotplot facilitates an educated judgement of how similar
two feature architectures are, and how likely it is that
the corresponding proteins are functionally equivalent.
Together with the implementation of four different scoring
functions and the BLAST search, the feature dotplot
complements the toolbox for a comprehensive search
for functional equivalents.
Conclusions
FACT uses the similarity of feature architectures
between two proteins to search for functional equivalents
in entire proteomes. FACT has a high fidelity and
outperforms existing approaches that identify functional
equivalents based on the presence of PFAM domains.
This increase in fidelity is mainly accomplished by using
a diverse set of features that are recruited for building
the feature architectures. The different weighting of
individual features and the relative position of shared
features in the compared proteins provide additional
information. FACT complements sequence similarity
based approaches, such as BLAST, in the search for proteins
with an equivalent function. It is, thus, particularly
useful when distantly related species with highly
diverged sequences are analyzed, or in cases where functional
equivalents are not homologous. Both aspects will
become increasingly relevant the more genome data
from ‘exotic’ species becomes available. However, there
exists no globally optimal solution to the problem of
identifying functionally equivalent proteins. It is therefore
necessary to compare the results from different
scoring functions measuring feature architecture similarity
and from sequence similarity based searches to select
the most promising functional equivalent candidates.
The feature dotplot to visually inspect the feature architectures
of two proteins facilitates this manual curation.
We have demonstrated the joint use of FACT, BLAST
and the feature dotplot in a comprehensive search for
functional equivalents in two example applications.
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They serve as a guideline of how to use these tools to
propagate existing knowledge about the function of proteins
from one species to another.
Methods
FACT
FACT annotates functional domains, secondary structure
elements and compositional properties in protein
sequences using the tools in the Sfinx package [33]. Low
complexity regions are identified with the program seq,
helices and strands with the program PHDseq, coiled
coils with the program COILS2, and signal peptides with
the program SignalP. Transmembrane regions are predicted
both with TMHMM and PHDhtm. Pfam (version
23; [34]) and SMART (smart_16_04_2008; [24]) domains
are identified with HMMER2 http://hmmer.janelia.org/
and regions enriched for a particular amino acid with
CAST [35]. Pfam clan information was downloaded
from http://pfam.sanger.ac.uk/. All annotation results
are transformed into the SFS format [33]. This data
structure allows for an easy extension of the feature set
with features currently not considered by FACT. For
sequence similarity searches BLAST version 2.2.13 and
PsiBLAST version 2.2.23 was used. PsiBLAST searches
were run with default parameter settings using 5 iterations.
The FDP is implemented as a Java applet requiring
Java 1.5 or higher. It can be accessed with a web
browser with Java and with Java script enabled.
Test set
We compiled the test set for the FACT evaluation using
an initial collection of 9897 EC annotated enzymes from
Homo sapiens (6,339), Arabidopsis thaliana (1,156), Saccharomyces
cerevisiae (1,099), Drosophila melanogaster
(896) and Caenorhabditis elegans (407). Protein
sequences were downloaded from Ensembl 52 (D. melanogaster,
C. elegans, S. cerevisiae), Ensembl 51 (H.
sapiens) and UniProt 1.0 (A. thaliana). The associated
EC annotations were retrieved from Ensembl and UniProt.
From this set we removed all proteins that were
annotated with more than one EC number or with partial
EC numbers. Subsequently, we discarded all EC numbers
and associated proteins which were present only once in
the protein collection. The final test set consists of 9,570
proteins representing 1,016 different EC numbers.
Data
Proteome data for Trypanosoma brucei was obtained
from the Sanger Center http://www.sanger.ac.uk. The
human glutathione S-transferase was identified in the
glutathion metabolic pathway in the KEGG database at
http://www.genome.jp/kegg/pathway/map/map00480.
html. The human protein ENSP00000196968 (Ensembl
51) was used as query for the FACT search in the yeast
proteome. For the GolgA5 search, the human protein
ENSP00000163416 (Ensembl 51) was used as query for
the FACT search in the T. brucei proteome.
Availability and requirements
Project name: FACT
Project home page: http://www.cibiv.at/FACT
Operating system: Platform independent
Programming language: Java
Other requirements: Java 1.5 or higher, java script
enabled
Additional material
Additional File 1: Supplementary Tables and Figures. Table S1:
Impact of p-value thresholds on the coverage of FACT (FACT score).
Figure S1: Impact of p-value thresholds on the fidelity of FACT (FACT
score). Figure S2: Cumulative fidelity along E-value thresholds for FACT
(FACT score), BLAST and the union of FACT and BLAST. Figure S3: Venn
diagram contrasting the performance of FACT (FACT score) and PsiBLAST.
Figure S4: Contrast of PsiBLAST and FACT (FACT score) for different Evalue/p-value
combinations. Figure S5: FDP of the human GolgA5 and
the highest scoring hit (MLS) in T. brucei: Tb927.5.1900. Figure S6: FDP of
the human GolgA5 and the highest scoring hit (MSuni) in T. brucei:
Tb11.02.4670. Figure S7: FDP of the human GolgA5 and the highest
scoring hit (MSst/FACT score) in T. brucei: Tb11.02.5040. Figure S8: FDP of
the human GolgA5 and the best BLAST hit in T. brucei: Tb11.52.0008.
Acknowledgements
The authors would like to thank Wolfgang Fischl for help with the Sfinx
installation, Huy Q. Dinh for helpful discussion, and Sascha Strauss, Jayne
Ewing and Gerard McGowan for critically reading the manuscript. This work
was supported by the Wiener Wissenschafts-, Forschungs-und
Technologiefonds (WWTF). TK is supported by the DFG priority program SPP
1174 Deep Metazoan Phylogeny [HA 1628/9 to A.v.H.].
Author details
1
Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz
Laboratories, Dr. Bohrgasse 9, A-1030 Vienna, Austria. 2
University of Vienna,
Vienna, Austria. 3
Medical University of Vienna, Vienna, Austria. 4
University of
Veterinary Medicine, Vienna, Austria.
Authors’ contributions
TK wrote the software and performed all analyses, IE conceived the study,
TK and IE designed the study and all three authors wrote the manuscript
and approved its final version.
Received: 15 March 2010 Accepted: 9 August 2010
Published: 9 August 2010
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doi:10.1186/1471-2105-11-417
Cite this article as: Koestler et al.: FACT: Functional annotation transfer
between proteins with similar feature architectures. BMC Bioinformatics
2010 11:417.
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