Nucleic Acids Research Advance Access first published online on May 30, 2007
This version published online on June 6, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm367
© 2007 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.
FRalanyzer: a tool for functional analysis of fold-recognition sequence–structure alignments
Harpreet Kaur Saini* and
Daniel Fischer
Computer Science and Engineering Department, 201 Bell Hall University at Buffalo, Buffalo, NY 14260, USA
*To whom correspondence should be addressed. Tel: 716 645 3180 (Ext. 163); Fax: 716 645 3464; Email: hksaini{at}cse.buffalo.edu
Received January 4, 2007. Revised April 26, 2007. Accepted April 26, 2007.
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ABSTRACT
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We describe FRalanyzer (Fold Recognition alignment analyzer),
a new web tool to visually inspect sequence–structure
alignments in order to predict functionally important residues
in a query sequence of unknown function. This tool is aimed
at helping to infer functional relationships between a query
sequence and a template structure, and is particularly useful
in analyzing fold recognition (FR) results. Because similar
folds do not necessarily share the same function, it is not
always straightforward to infer a function from an FR result
alone. Manual inspection of the FR sequence-structure alignment
is often required in order to search for conservation of functionally
important residues. FRalanyzer automates parts of this time-consuming
process. FRalanyzer takes as input a sequence–structure
alignment, automatically searches annotated databases, displays
functionally significant residues and highlights the functionally
important positions that are identical in the alignment. FRalanyzer
can also be used with sequence-structure alignments obtained
by other methods, and with structure–structure alignments
obtained from structural comparison of newly determined 3D-structures
of unknown function. Fralanyzer is available at
http://fralanyzer.cse.buffalo.edu/.
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INTRODUCTION
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As the genome sequencing and metagenomics projects are continuing
to deliver huge numbers of new protein sequences, the percentage
of sequences characterized as ‘unknown function’
is growing rapidly (
1,
2). In addition, as a result of Structural
Genomics, there is also an increasing number of proteins with
known structure but with no functional information (
3). Functional
characterization of a new protein can be based on sequence similarity
to a family of well-characterized proteins (
4,
5). In most cases,
when the sequence similarity between the query protein and a
well-characterized protein sequence is high, an automatic transfer
of function can be made. However, when the similarity is lower,
this automatic ‘annotation’ can be misleading and
other methods are needed. One method is based on template searching,
where the query sequence is searched for sequence motifs, patterns
or fingerprints as defined in databases such as Prosite (
6)
and InterPro (
7). Other methods attempt to transfer Gene Ontology
terms (
8–10) or EC terms (
11) to a new sequence. However,
such a transfer is only reliable when there is a clear match
to a homolog of known function. Although 3D-structural information
is often very valuable in understanding protein function, there
is an increasing number of proteins with known structures but
with unknown functions. Recently, new methods for functional
inference for such 3D-structures have been developed (
12). FR
methods lie in the midpoint between sequence-homology and 3D-structural
similarity approaches. In the lack of significant sequence similarity
and of an experimental 3D-structure, FR methods are often able
to identify compatible 3D-templates with relatively accurate
sequence–structure alignments (
13). Because the compatible
template is often characterized functionally, a FR result can
be a rich source of functional information (
14,
15). Functional
transfer from a template to the query sequence can be attempted
using the functionally significant residues in the template
that are conserved in the FR sequence–structure alignment.
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METHOD
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FRalanyzer (Fold Recognition alignment analyzer) is a web tool,
aimed at visualizing and annotating sequence–structure
alignments. Functionally important residues are displayed and
highlighted if they are conserved in the alignment. Functional
residues in a query sequence of unknown function are identified
based on the conservation of functionally significant residues
in sequence–structure and structure–structure alignments.
Thus, the input to FRalanyzer is an alignment, obtained independently
(i.e. not a part of FRalanyzer). To facilitate the human interface
and to avoid the need of cutting and pasting of alignments,
FRalanyzer can directly retrieve the alignments from other tools.
For FR sequence–structure alignments, a user can enter
the job ID of the (independently generated) FR results from
the Bioinfo Meta server (
16) or the FR Inub server (
17). FRalanyzer
displays a copy of the FR server results with an additional
column labeled as ‘Fralanyzer’ which consists of
the PDB codes of the templates identified by the FR server.
A sequence–structure alignment is defined by selecting
one of the PDB codes. For structure–structure alignments,
FRalanyzer accepts the results of the VAST server, which are
displayed so that the user selects one of the templates as before.
FRalanyzer displays the selected alignment along with a number of options. A sample output is shown in Figure 1. We submitted the conserved hypothetical protein EF3048 to Bioinfo, which returned the Bioinfo ID ‘49416’. We used this ID as input to FRalanyzer (see http://fralanyzer.cse.buffalo.edu/samples/ for the initial output window as well as our online documentation files), and selected the first template (PDB 1w1a). Figure 1 shows the main FRalanyzer output. Along with the alignment, the sequence identity % and the secondary structure identity of the aligned query and template are shown (14 and 70%, respectively). Identical residues and identical secondary structure states in the query and template sequences are highlighted. The secondary structure of the query and template are obtained from the FR server and/or PDBSum (18).
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Figure 1. FRalanyzer results of the alignment of the conserved hypothetical protein EF3048 with the 3D-structure of pdaa protein (PDB code 1w1a). The alignment was obtained from the Bioinfo FR meta-server, (job ID 49416). For clarity, only the first 120 residues are shown (see our ‘samples’ url for the full alignment). The predicted secondary structures (H and E) of EF3048 are shown above its sequence. The experimental secondary structure of 1w1a is shown below its sequence. Identical residues are highlighted. PDBSum features of the template such as active site residues, contacts to metal and ligands are shown in separate rows and are marked as asterisks or hyphens. 1w1a belongs to the carbohydrate esterase family 4, and contains a conserved D and 3 H residues (D73, H124, H128 and H222), which interact with the substrate. Other active site residues (e.g. G75) annotated by PDBSum line the active site groove. The FRalanyzer results highlight the conservation of the residues D73, G75, H124 and H222. The alignment also shows that H128 is shifted 2 residues from H93 in the query, probably due to a misalignment in the FR result. Thus, by quickly visualizing the FR alignment, we are able to derive a verifiable hypothesis that identifies EF3048’s catalytic residues D12, H64, H93 and H215, suggesting that EF3048 is also a member of the carbohydrate esterase family 4.
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Further automatic annotation of the alignment can be obtained
from functional features of the template from the PDBSum and
the SwissProt databases (
19). PDBSum functional features include
ConSurf residue conservation, active site residues, Prosite
motifs, contacts to DNA, ligands, metals and H-bonds to ligands.
Users can select any of the available features or all. Matched
Prosite motifs are highlighted in the target sequence in bold
red fonts. There are two options to obtain PDBSum features:
‘To Bring’ (access the PDBSum site) and ‘Local
Copy’ (access local copies instead, if they exist). In
the present example, the PDBSum features, active site residues,
contacts to ligand, metal and Hbonds to ligand are retrieved
and the respective checkboxes are checked (
Figure 1). Each feature
is displayed in a separate row with 1-letter amino acid of the
template (if present in the template) or ‘-’s (if
present in a structural homolog). In addition, the functionally
important positions that are conserved in the alignment (aligned
to identical residues in the query) are further highlighted
with vertical bars. For example, see the template active site
residues (D
73, G
75, H
124 and H
128) highlighted in
Figure 1.
Other possible annotations include: catalytic CSA, Prosite patterns,
ligand type or residue conservation. For instance, the ligand
types in the present example are GOL and NDG. The residue conservation
of the template is displayed as colored bars according to the
Consurf (
20) coloring scheme, where the most variable positions
are colored turquoise, intermediately conserved positions are
colored white and the most conserved positions are colored maroon.
The user can access further functional information for the template or the query from SwissProt. If selected, a BLAST search is executed to identify the SwissProt sequences that are most similar (if any). Features are extracted from the SwissProt FT lines from entries having sequence identity >90%.
Other FRalanyzer links
Currently, FRalanyzer has links to COGNITOR (21), to Meta-DP for domain prediction (22) and to Meta-GO for functional inference (unpublished).
Two sample predictions for proteins of unknown function
Our example shown in Figure 1 corresponds to a prediction for the Hypothetical UPF0249 protein EF3048, a protein of unknown 3D-structure. To identify a compatible template and to generate a sequence–structure alignment, we submitted this sequence to Bioinfo, which identified the structure of the hydrolase PDAA (PDB code 1w1a; a carbohydrate esterase) as a compatible template (with a very reliable score; see our ‘samples’ url). To determine whether any functional inference can be obtained, we analyzed the sequence–structure alignment. The alignment shows (Figure 1) that a number of the active site residues of 1w1a annotated in PDBSum are aligned to identical residues in the query. Based on this conservation of functionally important residues, the following hypothesis may be worthwhile to analyze in further detail: the conserved residues of EF3048 correspond to active site residues, and EF3048 is functionally related to the carbohydrate esterase family (see legend of Figure 1). The second example corresponds to a functional prediction obtained for the recently determined structure of a hypothetical protein of unknown function (PDB code 1ylo). We first identified structural homologs using VAST. FRalanyzer was used to visualize the structural alignment and to identify possibly functional important residues in the query (see our ‘samples’ url). Based on the conservation of functionally important residues, a plausible hypothesis locating the active site residues of 1ylo and its possible function as a peptidase can be postulated.
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CONCLUSION
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Predicting functionally important residues of a protein of unknown
function is a useful step in characterizing it. An effective
way to is to identify conserved functional key residues in an
alignment with a template of known 3D-structure. If an experimental
structure exists for the query protein, the alignment can be
obtained from structural alignments. Otherwise, it can be obtained
by e.g. sequence searches or FR. Relevant functional features
of the query can be predicted based on the functional features
of the template. FRalanyzer was developed to help in this process.
FRalanyzer automatically extracts important functional residues
from annotated databases and displays them along with the sequence–structure
alignment, highlighting the conserved positions. This helps
the user to quickly locate functionally important residues in
the query, which in turn can help in its functional characterization.
FRalanyzer can also be useful in the visualization of structural
alignments to known templates of newly determined proteins of
unknown function. Future versions may include links and automatic
accesses to other annotated databases and servers, as well as
graphical enhancements, faster response times and distinction
between different types of ligands.
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ACKNOWLEDGEMENTS
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The Fischer lab is supported by grants from the NIH and Genefun.
We thank the developers of PDBSum and Roman Laskowski for his
support. Funding to pay the Open Access publication charges
for this article was provided by a grant from the NIH.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION