SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs.

Abstract

Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein-protein interactions. The Short, Linear Motif Finder (SLiMFinder) web server is a de novo motif discovery tool that identifies statistically over-represented motifs in a set of protein sequences, accounting for the evolutionary relationships between them. Motifs are returned with an intuitive P-value that greatly reduces the problem of false positives and is accessible to biologists of all disciplines. Input can be uploaded by the user or extracted directly from UniProt. Numerous masking options give the user great control over the contextual information to be included in the analyses. The SLiMFinder server combines these with user-friendly output and visualizations of motif context to allow the user to quickly gain insight into the validity of a putatively functional motif. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Returned motifs can also be compared with known SLiMs from the literature using CompariMotif. All results are available for download. The SLiMFinder server is available at: http://bioware.ucd.ie/slimfinder.html.

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SLiMFinder: a web server to find novel, significantly
over-represented, short protein motifs
Norman E. Davey1, Niall J. Haslam2, Denis C. Shields2and Richard J. Edwards3,*
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg,
Germany,2UCD Complex and Adaptive Systems Laboratory, UCD Conway Institute, and School of Medicine
and Medical Sciences, University College Dublin, Dublin 4, Ireland and3School of Biological Sciences,
University of Southampton, Southampton, UK
Received February 10, 2010; Revised April 28, 2010; Accepted May 8, 2010
ABSTRACT
Short, linear motifs (SLiMs) play a critical role in
many biological processes, particularly in protein–
protein interactions. The Short, Linear Motif Finder
(SLiMFinder) web server is a de novo motif discov-
ery tool that identifies statistically over-represented
motifs in a set of protein sequences, accounting for
the evolutionary relationships between them. Motifs
are returned with an intuitive P-value that greatly
reduces the problem of false positives and is ac-
cessible to biologists of all disciplines. Input can
be uploaded by the user or extracted directly from
UniProt. Numerous masking options give the user
great control over the contextual information to be
included in the analyses. The SLiMFinder server
combines these with user-friendly output and visu-
alizations of motif context to allow the user to
quickly gain insight into the validity of a putatively
functional motif. These visualizations include align-
ments of motif occurrences, alignments of motifs
and their homologues and a visual schematic of
the top-ranked motifs. Returned motifs can also be
compared with known SLiMs from the literature
using CompariMotif. All results are available for
download. The SLiMFinder server is available at:
http://bioware.ucd.ie/slimfinder.html.
INTRODUCTION
The
(SLiMFinder) web server is to allow researchers to identify
novel Short Linear Motifs (SLiMs) in a set of sequences.
SLiMs, also referred to as linear motifs or minimotifs, are
functional microdomains that play a central role in many
diverse biological pathways (1). SLiM-mediated protein
interactionsincludepost-translational
purposeoftheShort, LinearMotifFinder
modification
(including cleavage), subcellular localization and ligand
binding (2). SLiMs are typically less than 10 amino acids
long and have less than five defined positions, many of
which will be ‘degenerate’ and incorporate some degree
of flexibility in terms of the amino acid at that position.
Their length and degeneracy gives them an evolutionary
plasticity which is unavailable to domains meaning that
they will often evolve convergently, adding new function-
ality to proteins (1). SLiMs hold great promise as future
therapeutic targets, which makes their discovery of great
interest (3,4).
Several web-based methods to discover novel instances
of known SLiMs are available such as ELM (2), MnM (5)
and Quasimotifinder (6). Proteins can be searched using
these methods to return putatively functional sites and
additional contextual information such as sequence con-
servation (7) and structural context (8) used to assess the
likelihood of true functional significance. Because of the
small, degenerate nature of SLiMs, stochastic occurrences
of motifs are common and distinguishing real occurrences
from random remains the greatest challenge in a priori
motif discovery. This challenge is increased further still
in de novo motif discovery as the motif search space is
considerably greater and often it is not even known
if the proteins being examined share a common SLiM
or not.
The concept of over-representation as an indicator of
functionality is currently the most powerful and widely
used approach for discovering de novo SLiMs computa-
tionally (9–13). A set of proteins for which there is a sus-
pected SLiM-mediated common function (e.g. targeting
protein localization, mediating protein binding or acting
as a recognition site for a post-translational modification)
is analysed under the hypothesis that the function-
mediating SLiM would occur more often than expected
by chance due to the selection for the motif in these
proteins.Such SLiM discovery algorithms therefore need
to overcome two obstacles: (i) identify over-represented
motifs; and (ii) assess whether such motifs are expected
*To whom correspondence should be addressed. Tel: 023 8059 4344; Fax: 023 8059 4459; Email: r.edwards@southampton.ac.uk
W534–W539
doi:10.1093/nar/gkq440
Nucleic Acids Research, 2010, Vol. 38, Web Server issuePublished online 23 May 2010
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
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by chance. Many algorithms are very good at (i) but not
(ii), i.e. they will find over-represented motifs that are
genuinely present in a data set but are poor at
discriminating these from false positives. This is particu-
larly true for algorithms that do not consider the evolu-
tionary relationships within the input proteins as results
will tend to be dominated by longer regions of conserva-
tion or homology (e.g. globular domains) at the cost of
SLiM detection.
Motif discovery algorithms can be broadly divided into
two types: alignment-based and alignment-free. Those
thatlook for motifs in
alignment-based approaches, are generally optimal for
discovery of very strong (or long) ‘family descriptor’
motifs [e.g. MEME (14) and PRATT (15)] or for improv-
ing definitions of known motifs [e.g. GLAM2 (16)]. More
successful de novo SLiM discovery methods (10–12) are
alignment-free and built on an explicit model of conver-
gent evolution, using over-representation of motifs in ‘un-
related’ proteins. Dilimot (13) and SLiMDisc (10)
combine these techniques with heuristic scoring schemes
to successfully discover new functional motifs and redis-
coverknownmotifs. Neduva
demonstrated the potential of models based on convergent
evolution when they applied Dilimot to discover SLiMs in
multiple HPRD data sets. These algorithms still struggled
to identify false-positive predictions, however.
SLiMFinder (11) extended these heuristics to estimate
the statistical significance of returned motifs, through
improved statistics that account for the background of
randomly recurring motifs, correcting for evolutionary re-
lationships within the data. The P-value returned by
SLiMFinder greatly reduces the problem of false positives
and provides a score that is intuitive and accessible to
biologists of all disciplines. Additional development of
this algorithm has seen further improvements in both sen-
sitivity and specificity through use of conservation-based
masking (17) and more accurate statistical models (18).
The SLiMFinder server combines these new features
with user-friendly input/output and visualizations of
motif context to allow the user to quickly gain insight
into the validity of a putatively functional motif.
related sequences,using
etal.(12) clearly
THE SLiMFinder ALGORITHM
SLiMFinder (11) is a probabilistic SLiM discovery
program building on the principles of the SLiMDisc algo-
rithm (10). As input, the algorithm takes a data set of
proteins with a common feature (e.g. common binding
partner) that might be SLiM-mediated. These proteins
can be masked to exclude under-conserved residues (17),
non-disordered regions predicted using IUPred (19), low
complexity regions, specific amino acids or motifs, and
annotated features including domains or user-annotated
regions to allow any contextual information to be
included in the analyses. The TEIRESIAS raw motif dis-
covery tool is (20) replaced by SLiMBuild (11) allowing
flexible and ambiguous motifs to be returned. Motifs are
built by combining dimers into longer patterns, retaining
only those motifs occurring in a sufficient number of
unrelated proteins. Motifs with fixed amino acid positions
are identified and then combined to incorporate amino
acid ambiguity and variable-length wildcards.
Statistics are implemented in the SLiMChance algo-
rithm (11), which is based on the binomial statistics
introduced by ASSET (21) [also used by Dilimot (13)]
with two major extensions: (i) homologous proteins are
weighted(as inSLiMDisc)
dependencies introduced into the probabilistic framework
by homologous proteins; and (ii) introduction of signifi-
cance scores, i.e. the probability that any motif considered
would reach a binomial P-value by chance is calculated
and used to rank motifs. This greatly increased the strin-
gency of de novo SLiM discovery, and substantially
reduced false-positive predictions (11). The original
SLiMChance algorithm incorporated some simplifying as-
sumptions for the sake of computational efficiency, with
the resulting tendency to increase returned P-values
slightly [i.e. increase the chance of a false-negative result
(18)]. Although full correction is not computationally ef-
ficient enough for the web server, a partial correction is
available at the cost of increased run times.
to accountfor the
THE SLIMFINDER WEB SERVER
The SLiMFinder server is available at: http://bioware.ucd
.ie/slimfinder.html. The purpose of the web server is to
allow researchers to identify novel SLiMs in a set of se-
quences. Sequences are first masked according to user spe-
cifications before recurring motifs are identified using the
SLiMBuild algorithm. The SLiMChance algorithm then
estimates statistical significance of recurring motifs and
the most significant, filtered according to user specifica-
tions, are returned. Interactive output permits easy explor-
ation and visualizations of motif context to allow the user
to quickly gain insight into the validity of a putatively
functional motif. The web server is powered by the same
code as the downloadable version of SLiMFinder and
details of the algorithm can be found in the previous pub-
lications (11,17,18). The novel features of the web server
are described in more detail in the following sections.
Input
SLiMFinder is optimized for, and requires, at least three
sequences for analysis. Two options are provided for
sequence input:
1. UniProt IDs can be used to extract entries directly
from the UniProtKB (22). Extracted entries are available
for download by the user for additional reference/analysis.
This is the preferred method of input and enables the full
functionality of the web server, including conservation-
based masking (17).
2. User-constructed sequences files can be uploaded or
pasted directly into the input form. UniProt flat files and
FASTA format sequences are accepted. UniProt entries
arerecommended forfull
options. Because conservation-based masking makes use
of pre-computed alignments, this is not available for
user-enteredsequences. To
feature-basedmasking
use conservation-based
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masking of user-entered sequences, it is recommended to
download SLiMFinder and run it locally.
Examples of all input formats are available in the help
pages of the web server. An example data set can also be
loaded directly into the input entry form for user experi-
mentation (see example analysis).
Options
Following sequence input, the user can run SLiMFinder
with default parameters, if desired. One of the strengths of
SLiMFinder, however, is the ability to tailor the options
to specific motif discovery needs. In particular, a large
range of masking options are available to exclude (or con-
centrate on) specific features. Options are divided into
Masking, SLiMBuild and SLiMChance/Output filtering
(Figure 1). All options are explained in both the online
help pages and the SLiMFinder manual; options are
named for consistency and ease of transition between
both web server and commandline implementations of
SLiMFinder.
Note that, by default, the server will return up to 100
motifs atP?0.99.
SLiMChance statistics are slightly conservative (18)
and returning more motifs gives the user more control
to determine what they think is interesting; for many
applications a high false-positive rate might be tolerated.
We urge extreme caution when interpreting motifs with
Sig > 0.5 as they are most probably over-represented by
chance and a much stricter cut-off (e.g. 0.05) should be
used when stringency and lack of false positives is
important.
This isbecausethedefault
Submitting jobs
Once options have been reviewed, clicking ‘Submit job’
will enter the run queue. Run times will vary according
toinputdatasize/complexity,
options and the current load of the server. Users can
either wait for their jobs to run, or bookmark the page
and return to it later.
Masking/SLiMBuild
Figure 1. Input options. Options are separated into sequence masking, SLiMBuild motif construction and SLiMChance/Output filtering. For clarity,
all options correspond to commandline parameters of downloadable SLiMFinder program; short descriptions and commandline parameter names
are given if the mouse hovers over the help buttons. All options are described in the help pages. Once options have been set/reviewed, ‘Submit job’
will move the job into the run queue.
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Output
The initial results page shows summary results for
returned motifs (Figure 2). The ‘Sig’ indicates the
estimated significance level of each motif. Clicking the
redlinksexpandsdetails
CompariMotif hits, while the ‘M’ and ‘A’ alignment
links will visualize the motif in the input proteins,
masked andunmasked,
Alignments for each protein and its GOPHER (9)
orthologues around the motif of interest can be accessed
by clicking on the ‘Plot’ link for each protein/motif pair
(Figure 2). Protein disorder and RLC scores are also
visualized in these alignments and the region can be
altered to zoom in or out as desired. All alignments can
be saved as PNG or high-quality PDF files. Returned
motifs are also compared with known literature motifs
usingCompariMotif(23)
orthologue alignments for each protein and motif maps,
as introduced by the SLiMDisc web server (9) are also
available. In addition to the visualizations, all results
fortheproteins and
respectively (Figure 2).
(Figure 3).Full-length
files normally generated by the commandline implementa-
tion of SLiMFinder can be downloaded as plain text for
further analysis and manipulations.
Example analysis
The web server incorporates a full example for a data set
of seven proteins containing manually curated, experimen-
tallyvalidated, Dynein light
([^P].[KR].TQT) taken from ELM entry LIG_Dynein_
DLC8_1 (2) in Uniprot format. A full walkthrough for
this data set is provided in the help pages and fully inter-
active example output is also provided. As previously
reported for SLiMFinder (11,17), the SLiMFinder web
server returns TQT and K.TQT as significant motifs
(P<0.01).
chainbindingmotifs
Getting help
The SLiMFinder web server is supported by an extensive
help section,including
walkthrough with screenshots. Example input files are
a quickstartguideand
Figure 2. Main results page. Summarized results for each motif are initially displayed. These can be expanded to reveal individual occurrences in
each protein for each motif. Alignments can be generated to explore the unmasked and masked sequence context for each motif ‘(M|A)’ or to
examine the region around a specific motif occurrence in a single protein ‘(Plot)’. All visualizations can be exported as PNGs or high-quality PDFs.
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provided and example input data can be loaded into the
input forms. Fully interactive example output (corres-
ponding to running the example input with default par-
ameters) is clearly linked from the help pages (see example
analysis). Additional details of the algorithms and options
can be found in the SLiMFinder manual, which is also
clearly linked from the help pages.
FUTURE WORK
The SLiMFinder server is designed to be flexible and allow
easy incorporation of future updates to the main
SLiMFinder program(currently
version number is clearly shown on the front page of the
web server and is stored in the log file of each run, be it
commandline or server based.
Appropriate use of conservation can significantly
increase the sensitivity of SLiMFinder (17). Currently,
conservation masking is
metazoan organisms and humans in particular. With
time, we hope to expand the range of taxonomic groups
for which conservation-based masking is available. These
will be added to the Masking options tab and appropriate
help pages.
version4.1). The
only wellsupportedfor
CONCLUSION
Technological
coverage of protein interaction networks but it is the
ability to add details to these networks—such as predic-
tions of interaction motifs—that will really enable them to
drive biological discovery. The P-value returned by
SLiMFinder is of fundamental importance to move
SLiM discovery out of the domain of a few computational
specialists and into the hands of experimental molecular
advanceshave greatly increasedthe
biologists. To facilitate this transition, we have imple-
mented SLiMFinder as an intuitive, interactive web
server that provides numerous useful visualizations for
data exploration without sacrificing any of the main
options offered by the commandline implementation.
The SLiMFinder server is available at: http://bioware
.ucd.ie/slimfinder.html.
FUNDING
Science Foundation Ireland and the University of
Southampton; European Molecular Biology Laboratory,
EMBL Interdisciplinary Postdoc (EIPOD) fellowship
(to N.E.D.). Funding for open access charge: Science
Foundation Ireland (grant 08/IN.1/B1864).
Conflict of interest statement. None declared.
REFERENCES
1. Diella,F., Haslam,N., Chica,C., Budd,A., Michael,S., Brown,N.P.,
Trave,G. and Gibson,T.J. (2008) Understanding eukaryotic linear
motifs and their role in cell signaling and regulation.
Front. Biosci., 13, 6580–6603.
2. Gould,C.M., Diella,F., Via,A., Puntervoll,P., Gemund,C.,
Chabanis-Davidson,S., Michael,S., Sayadi,A., Bryne,J.C., Chica,C.
et al. (2010) ELM: the status of the 2010 eukaryotic linear motif
resource. Nucleic Acids Res., 38, D167–D180.
3. Kadaveru,K., Vyas,J. and Schiller,M.R. (2008) Viral infection and
human disease–insights from minimotifs. Front. Biosci., 13,
6455–6471.
4. Neduva,V. and Russell,R.B. (2006) Peptides mediating interaction
networks: new leads at last. Curr. Opin. Biotechnol., 17, 465–471.
5. Rajasekaran,S., Balla,S., Gradie,P., Gryk,M.R., Kadaveru,K.,
Kundeti,V., Maciejewski,M.W., Mi,T., Rubino,N., Vyas,J. et al.
(2009) Minimotif miner 2nd release: a database and web system
for motif search. Nucleic Acids Res., 37, D185–D190.
Figure 3. CompariMotif results. All motifs returned by SLiMFinder are cross-referenced against known motifs using CompariMotif, enabling easy
identification of re-discovered known motifs. All columns are sortable by clicking on their respective headings and more information can be revealed
about motifs and their CompariMotif hits by mousing over the appropriate data.
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