Published online 8 October 2008Nucleic Acids Research, 2009, Vol. 37, Database issue D611–D618
PMAP: databases for analyzing proteolytic events
Yoshinobu Igarashi1, Emily Heureux1, Kutbuddin S. Doctor1, Priti Talwar1,
Svetlana Gramatikova1, Kosi Gramatikoff1, Ying Zhang1, Michael Blinov3,
Salmaz S. Ibragimova2, Sarah Boyd4, Boris Ratnikov1, Piotr Cieplak1,
Adam Godzik1, Jeffrey W. Smith1, Andrei L. Osterman1and Alexey M. Eroshkin1,*
1The Center on Proteolytic Pathways, The Cancer Research Center and The Inflammatory and Infectious Disease
Center at The Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA,
2Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Lavrentieva 10,
Novosibirsk 630090, Russia,3Center of Cell Analysis and Modeling, University of Connecticut Health Center,
Farmington, CT 06030, USA and4Faculty of Information Technology, Monash University, Clayton, Victoria 3800,
Received August 15, 2008; Revised September 19, 2008; Accepted September 23, 2008
proteolysis.org) is a user-friendly website intended
to aid the scientific community in reasoning about
proteolytic networks and pathways. PMAP is com-
prised of five databases, linked together in one
environment. The foundation databases, Protease-
DB and SubstrateDB, are driven by an automated
annotation pipeline that generates dynamic ‘Mole-
cule Pages’, rich in molecular information. PMAP
also contains two community annotated databases
focused on function; CutDB has information on
more than 5000 proteolytic events, and ProfileDB
is dedicated to information of the substrate recog-
nition specificity of proteases. Together, the content
within these four databases will ultimately feed
PathwayDB, which will be comprised of known
pathways whose function can be dynamically mod-
eled in a rule-based manner, and hypothetical path-
ways suggested by semi-automated culling of the
literature. A Protease Toolkit is also available for
the analysis of proteases and proteolysis. Here, we
describe how the databases of PMAP can be used
to foster understanding of proteolytic pathways, and
equally as significant, to reason about proteolysis.
Proteolysis MAP(PMAP, http://www.
Regulatory proteolysis is an important and unique type of
posttranslational modification because it is irreversible.
Proteolysis is essential to almost all fundamental cellular
processes including proliferation, death and migration
(1–4). Equally as important, mis-regulated proteolysis
can cause diseases ranging from emphysema (5) and
thrombosis (6), to arthritis (7) and Alzheimer’s (8). There
are a number of online resources containing information
on proteases including SwissProt (the oldest), HPRD
(human protein reference database) (9) and UniProt (10).
The best known protease resource, MEROPS (11) provides
a ‘gold standard’ in protease classification, and basic infor-
mation on almost 100 000 proteases. However, none of
these resources address the predictive modeling of proteo-
lytic events or the analysis of proteolytic networks. The
Proteolysis MAP (PMAP) reasoning environment was
established with these objectives in mind. It aims to take
advantage of information available in public databases,
results from experiments, the users own imagination to
perform specific queries, and then brings these elements
together to efficiently address dependent queries like:
(i) I know of a protease that is up-regulated in a spe-
cific disease. What are the likely substrates for this
protease that could drive the pathology?
(ii) I have identified a protein that is cleaved during a
biological event, and I know the position of the cut
site. Which proteases are likely to be responsible for
(iii) I know of a protease and its substrate that are
necessary for a biological event. What other pro-
teins might associate with these two in a regulatory
(iv) I have identified a cut site within a protein, but
I have indications that such cleavage is regulated.
*To whom correspondence should be addressed. Tel: +1 858 646 3100/3923; Fax: +1 858 713 9949; Email: email@example.com
? 2008 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.
within the protein that are likely to regulate
(v) I have found two compounds that interfere with a
proteolytic pathway in apparently different time-
regimes. Can I quantitatively model the pathway
to identify their potential protein targets and gain
insight into differences in their behavior?
(vi) I have a virus protease that participates in matura-
tion of viral proteins and want to know if it may
also have some human substrates.
Within the context of PMAP, these queries are addressed
by bringing together seemingly unrelated or physically dis-
connected information that is now stored in a set of five
interacting databases: ProteaseDB, SubstrateDB, CutDB,
ProfileDB and PathwayDB. Their integration is sufficient
to make them of great value to the research community.
DATABASES OF PMAP
ProteaseDB contains basic information for a set of ?150
human proteases deemed to be of high interest. The data-
base actually contains more than 45000 proteases
acquired from MEROPS, and information on this com-
prehensive set of proteases will be displayed in the form of
Molecule Pages as the site matures. The information
is stored in a MySQL database and presented as a
Molecule Page on a web server (Figure 1, top, as an exam-
ple). Each Molecule Page displays a comprehensive set of
annotations on 15 different features of proteases, all
acquired from an automated protein annotation pipeline
run on a Linux cluster. These features include predictions
of PFAM domain structure, secondary structure, trans-
membrane regions, signal peptides and disordered regions.
Among the methods used in the protein annotation pipe-
line are: BLASTP, protein sequence homology search (12);
Hmmer, hidden Markov model search (13); TMHMM,
transmembrane helices prediction (14); SignalP, signal
peptide prediction (15); Jnet, secondary structure predic-
tion (16); Coils, coiled-coil region prediction (17); Seg, low
complexity segment identification (18); MODELLER,
homology modeling of protein 3D structures (19). The
3D structure, or high-resolution model, of each protease
is presented within a Jmol viewer capable of querying and
displaying many structural features. Each Molecule Page
is also linked to external sites (MEROPS, PDB, PubMed,
GeneCards and the GNF SymAtlas) from which data was
collected and where additional information can be found.
Molecule Pages present ‘Recent News and Literature’,
which shows selected articles from PubMed related to
the protease via a dynamic web services query (see
Supplementary Material). If entries for a protease are
present in CutDB, a simplified list of substrates is present,
SubstrateDB contains molecular information on docu-
mented protease substrates in CutDB. SubstrateDB differs
from ProteaseDB in that it is designed to map a variety of
annotations onto the primary sequence of known or pre-
dicted substrates (Figure 1, bottom). The database archi-
tecture allows other posttranslational modifications to be
mapped onto the primary sequence of the substrate.
Documented protease cleavage sites are visible within
the context along with all other potential regulatory
To achieve this type of dynamic mapping, and to keep it
current, SubstrateDB dynamically collects data from
PMAP, UniProt and InterPro [via web services APIs
(20)]. Substrate pages are constructed at the request of
the user. To reduce the wait-time for gathering and inte-
grating information, the system retains a dynamic cache of
data from prior requests. Information within SubstrateDB
is displayed onto the primary structure of the substrate
using a Java-script driven interface adapted from the
Simile project hosted by CSAIL (MIT/Broad Institute,
(http://simile.mit.edu/timeline/). We have adapted the
Simile’s Timeline interface to plot data along a one-
dimensional axis corresponding to the primary sequence
of the substrate. The Timeline interface allows users to
dynamically select subsets of annotation for display.
CutDB is a database of individual proteolytic events
(cleavage sites) culled from the literature. It represents
one of the first systematic efforts to build a collection of
documented proteolytic events (21), and as such, is the
largest database of proteolytic events in the world. The
database has more than 5300 annotated proteolytic
events that occur within 1702 protein substrates. The
annotated proteolytic events are enacted by 180 serine,
164 metallo, 108 cysteine and 61 aspartic proteases. The
seed data for CutDB (?2000 substrates) was extracted
from MEROPS, HPRD and UniProt. Then ?3000 addi-
tional substrates were extracted from original articles by a
combination of directed literature searches and readings
by human experts.
Each proteolytic event is annotated as a cut site location
within the primary sequence of the protein substrate. The
residues spanning the cut site are highlighted. Events
are annotated with core information including the mole-
cular identity of the protease and substrate which are each
linked to their pages within ProteaseDB and SubstrateDB.
Each event is also connected to Literature Track, which
has information of the articles (PubMed IDs including
links) used to annotate the event. When available, other
features associated with the event are also stored in
CutDB. These include information on the method by
which the event was detected, the potential consequences
of the event, relevant cofactors, associated pathways, cell
compartments where the event takes place, cell lines where
the event is observed and information linking the event to
any process or disease.
Any registered user can curate the content of CutDB
by adding new events, fixing errors or adding comments.
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
The comment section is divided into several categories,
such as ‘discussion’, ‘hypothesis’, ‘drugs in development’
and ‘other comment’. Manual annotation is already being
conducted using the web interface by users throughout the
world. This database will soon be extended to include
proteolytic events detected by mass spectrometry and by
statistical predictions (see ProfileDB section).
ProfileDB is a protease specificity profiling database that
contains information on substrate recognition from phage
display experiments and other systematic studies, like pep-
tide libraries. It has tools for using these data to predict
protease cleavage sites in proteins and a screening engine
for searching potential substrates in the whole human
Figure 1. Protease Molecule Page (top) and Substrate Molecule Page (bottom). The Protease Molecule Page shows recent news and literature of the
protease (top left), known proteolytic events (top right), domain location and structure view (middle panel), as well as a cross annotation in other
databases section (bottom). A graphics interface of SubstrateDB (bottom) is shown here displaying domains and experimental protease cut sites for
the human amyloid b A4 protein. A total of 35 protease cut sites are filtered via selection boxes on the right. The display makes co-location of many
features visible. The annotation displayed is linked back to sources (CutDB, ProteaseDB, UniProt, etc.) for more detailed annotation. The graphics
display is synchronized with a text table of the same data, which can also be downloaded.
Nucleic Acids Research, 2009,Vol. 37,Database issueD613
proteome. This database also supports community-based
input and analysis. ProfileDB allows the user to search the
database for a particular data set and submit specificity
profiling data, so that the results from this work become
easily accessible to other scientists. Using the database
interface, it is easy to create protease specificity models
(position weight matrix or PWM) and predict substrate
cleavage sites using other specificity models or specificity
motifs in a single substrate or fasta file. The methods we
use to compute PWM and to predict protease cleavage
sites have been previously described (22). An important
feature of our proteome-wide search is the ability to
filter proteome prediction by multiple criteria, like protein
localization, cleavage site secondary structure or the pre-
sence of disordered regions.
A simple scenario of how a user submits a proteome-
wide search for substrates is shown in Figure 2A. After
selection of the ‘do predictions’, the user will receive the
result table of prioritized (by PWM score) potential sub-
strates (Figure 2B) and, finally, the details of the cleavage
site, including the amino acid sequence, protein secondary
structure, disorder regions and Pfam domains (Figure 2C).
The front page of the database presents raw profiling data
for seven matrix metalloproteinases (MMPs, studied by
The Center on Proteolytic Pathways), prebuilt PWM
matrices for each of MMPs and proteome-wide prediction
PathwayDB is the most recent addition to PMAP and is
still in its formative stages. This database contains three
types of information on proteolytic pathways and
networks: (i) pathways reconstructed in silico using
Figure 2. ProfileDB proteome-wide substrate search. (A) Initial page where the user selects options for proteome screening. (B) A results table
generated from the proteome-wide screening with a detailed output for a particular protein. (C) Presentation of the cleavage site on the amino acid
sequence with protein sequence features (secondary structure and disorder regions location).
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
proteolytic event(s) as an initial seed, (ii) protease inferred
networks reconstructed by a focused lexical application of
Cytoscape (23) and (iii) scenarios of the function of pro-
teolytic pathways derived from rule-based modeling.
PathwayDB stores and displays, upon user request,
pathways previously constructed from each of these
scenarios. PathwayDB is a community-driven database
where a registered user can create or modify pathways.
Though the current content of PathwayDB is limited,
it is continuously being expanded by the group of curators
at The Center on Proteolytic Pathways, and by outside
in silico by using proteolytic events from CutDB as a
seed. The user can select an event, or a series of proteolytic
events, and the system constructs a network diagram in
which the nodes correspond to proteases and substrates
(Figure 3A). Hence, one can easily visualize all of the
substrates for a given protease, or even all of the proteases
that cleave a given substrate. These networks can be
expanded to another level by including protein–protein
interaction data taken from the IntAct database and
homology information from UniRef (24). This allows a
vastly simplified visualization of the network and elimi-
nates a great deal of redundancy. In the network diagrams
that are generated, the substrate names are converted
into HUGO gene symbols (25), which are stored in
NCBI RefSeq records. Reconstructed network can be
submitted for storage to PathwayDB (with appropriate
title and description given by the author) and retrieved
by any user.
proteolyticpathways are reconstructed
Proteolytic inferred networks
PathwayDB contains inferred networks in the form of
a ‘Network of the month’. These networks are developed
using a focused application of Cytoscape (23). Ultimately,
PMAP will provide users with the opportunity to con-
struct networks in the same manner. The ‘Network of
the month’ is a natural language processing (NLP)-based
and author-driven network of molecular events including
proteolysis of interest. It is extracted from natural lan-
guage text such as PubMed abstracts, and is represented
in a hierarchical hyper-graph data structure (23,26). By
adding this new capability to Cytoscape, we are able to
extract information on proteolytic events from Medline
and automatically reconstruct protease inferred networks.
The example network shown in Figure 3B was constructed
starting from a simple two-word query (e.g. ‘complement
AND proteolysis’) to PubMed. GO names found in the
resulting 396 MEDLINE abstracts were imported into
Cytoscape to generate the literature-supported inferred
Rule-based modeling ofnetwork function
In some cases, a great deal of knowledge already exists for
a network or pathway, and the user is interested in know-
PathwayDB has implemented a rule-based approach
using the BioNetGen framework (27) to evaluate such
queries (Figure 3C). Initially, all distinct states of reactant
species and complexes are calculated using the BioNetGen
framework. Then, a quantitative model is generated that
encompasses possible reactions implied by the nature
of interactions, using the initial reactant species and
an expanded set of molecular species (complexes).
Individual reactions in the previous step are further para-
meterized with rate constant data coming from the corre-
sponding rules. The user has the capability of viewing the
detail parameters of the underlying default model. In addi-
tion, the rules and parameters of the default model can
be modified to generate advanced models. We developed
an upload system, where the user can submit a rule-based
model with description, data files and a model diagram to
PathwayDB. The uploaded model immediately becomes
available for other users of the system. In the future, we
aim to generate a framework for using these pathway
models to assist the user in making functional hypotheses
in specific proteolytic pathways.
There are numerous applications of PMAP. Subsequently,
we briefly outline how the databases of PMAP can be
applied to address point-by-point the dependent queries
put forth in the Introduction section:
Workflow. First the user can search CutDB, to determine
if there are any substrates for the protease that have been
reported in the literature. In the absence of any known
substrates, the user can use ProfileDB to determine
if the substrate recognition profile of the active site
has been determined. If so, ProfileDB can be used to iden-
tify likely, or predicted, substrates for the protease of
Workflow. The user can perform a query of CutDB to
determine if the protein is a known substrate for any pro-
teases. If there are multiple proteases that cut the protein,
then the information in CutDB will point to the protease
responsible for a particular cut site. If there is no docu-
mented protease that cleaves the protein of interest, then
the user can search ProfileDB to determine if the protein is
a predicted substrate for any protease.
Workflow. The user can take two approaches toward this
question. First, the user can search CutDB for all the
substrates for the protease, and all of the potential pro-
teases that cleave the substrate. The user can ask
PathwayDB to link all of these proteases and substrates
in a network map. The user can also ask CutDB to extend
the association map to include known protein–protein
interactions. Alternatively, the user can request that the
PMAP team apply the inferred network feature of
PathwayDB to identify abstracts within PubMed that
link the protease and substrate or any other useful
Nucleic Acids Research, 2009,Vol. 37,Database issueD615
search term. Then, PathwayDB can be directed to funnel
this information into Cytoscape to arrive at a network
map. We anticipate that the number of these inferred
pathways stored within the database will grow as more
users take advantage of the site, but consideration is
also being given to providing a semi-automated workflow
of this type to the user.
Workflow. To address this question, the user can access
SubstrateDB and pull forward the page associated with
the substrate of interest. Here, the user can paint the
substrate with a wide range of detailed molecular informa-
tion, including the position of domains and posttransla-
Figure 3. Three types of information in PathwayDB. (A) Automated network reconstruction for one proteolytic events caused by ADAM17
peptidase and Notch1 preproprotein. Left panel: from CutDB front page, users search for the events. Middle panel: selection of events to reconstruct
networks. Users can also define three parameters to show the network diagrams: (i) ID display mode; (ii) flexibility of connection, the nodes are
connected within given threshold of sequence similarity; and (iii) steps of expanding, the edges are extended by given extension level. Right panel: the
reconstructed pathway. The red arrows indicate the selected events. The black arrows indicate expanded events. The nonarrows edges are protein–
protein interactions. (B) Proteolytic inferred networks. Left panel: PubMed-XML writer, a web service for extracting Medline abstracts, is used to
delimit lexical queries, for example ‘complement AND proteolysis’. These abstracts are then assembled in XML format and submitted to Agilent
Literature Search, acting as plug-in to Cytoscape (middle panel), which parses sentences containing ontologically known entities (such as gene and
protein names). Cytoscape takes these gene/protein names and builds interactive networks as hyper-graphs. Right panel: hyper-graph generated for
one ‘Network of the month’ entitled ‘Complement activation and regulation 01’. This network captured five proteases (nodes in yellow) that interplay
with six substrates (nodes in green), four cofactors (nodes in light yellow), two inhibitors (nodes in red) and other binding proteins such as C4BP
(white nodes). (C) Rule-based modeling of network function. Left panel: schematic representation of major events involved in coagulation. Middle
panel: rules for coagulation cascade. Right panel: modeling of production/consumption profiles of individual components of the pathway using an
in-house developed rule-based model for coagulation. In the plot, the x-axis denoted the time of simulations (100 iterations) and the y-axis is the
molecular concentrations in nanomolar (0–100000).
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
Workflow. The user can apply the rule-based modeling
feature of PathwayDB, and apply various constraints,
including those that occur in response to the inhibitors.
Rule-based modeling within PathwayDB will allow the
user to generate perturbed models. These models then
can be further studied using for example virtual cell envir-
onment (28) to decipher quantitative changes of the
underlying pathway and thus allowing the user to ‘visua-
lize’ the potential consequences of perturbation.
Workflow. The user can start with the submission of
known virus cleavage sites to ProfileDB, followed by
development of a protease specificity model using known
cut sites and prediction of potential substrates in an entire
human proteome. The user can then look for tissue-
related, physiologically relevant proteins in a prioritized
list and check experimentally if they can be cleaved
PMAP ARCHITECTURE AND IMPLEMENTATION
The ProteaseDB and SubstrateDB applications use the
Catalyst web framework, whereas CutDB uses the Ruby
on Rails web application framework. Other tools use a
variety of frameworks and languages. The PMAP applica-
tion that integrates components and links these compo-
nents together was build using the Perl-based Catalyst web
framework. Catalyst web framework allows us to separate
the components of our system into three parts: the Model,
the View, and the Controller (MVC). The Model repre-
sents the data of the application, the View specifies the
user interface, and the Controller handles communication
among all elements of the application.
Simple Object Access Protocol (SOAP)-based web
services have been used to integrate the different database
applications in PMAP. SOAP has been effective in allow-
ing simple, reliable connections between diverse resources.
In one case, to list substrates for a particular protease
(from Ruby on Rails), and to grab the most current lit-
erature from PubMed related to that protease (from
Windows ASP) and deliver these integrated results
within ProteaseDB page (via Perl, Catalyst).
In CutDB and PathwayDB, all frameworks for the
web interface are implemented using Ruby on Rails.
The database in the background is MySQL. The web
server is Lighttpd. The network diagram is generated by
In the future, we will continue to programmatically inte-
grate the separate databases of the PMAP project, add
user annotation, and develop it in such a way that we
will be able to add functionality and maintain code as
efficiently as possible.
Supplementary data are available at NAR Online.
We thank many at the Burnham Institute for Medical
Research for the data curation and Dr Weizhong Li for
providing protein annotation pipeline. We thank Dr
Christina Niemeyer for her editorial assistance and critical
review of the manuscript.
National Institutes of Health (RR020843, CA108959,
CA30199). Funding for open access charge: National
Institutes of Health (RR020843, CA108959, CA30199).
Conflict of interest statement. None declared.
1. King,R.W., Deshaies,R.J., Peters,J.M. and Kirschner,M.W. (1996)
How proteolysis drives the cell cycle. Science, 274, 1652–1659.
2. Kudo,N.R., Wassmann,K., Anger,M., Schuh,M., Wirth,K.G.,
Xu,H., Helmhart,W., Kudo,H., McKay,M., Maro,B. et al. (2006)
Resolution of chiasmata in oocytes requires separase-mediated
proteolysis. Cell, 126, 135–146.
3. Salvesen,G.S. and Dixit,V.M. (1997) Caspases: intracellular signal-
ing by proteolysis. Cell, 91, 443–446.
4. Saffarian,S., Collier,I.E., Marmer,B.L., Elson,E.L. and Goldberg,G.
(2004) Interstitial collagenase is a Brownian ratchet driven by pro-
teolysis of collagen. Science, 306, 108–111.
5. Barnes,P.J., Shapiro,S.D. and Pauwels,R.A. (2003) Chronic
obstructive pulmonary disease: molecular and cellular mechanisms.
Eur. Respir. J., 22, 672–688.
6. Carrell,R.W. and Owen,M.C. (1985) Plakalbumin, alpha
1-antitrypsin, antithrombin and the mechanism of inflammatory
thrombosis. Nature, 317, 730–732.
7. Holmbeck,K., Bianco,P., Caterina,J., Yamada,S., Kromer,M.,
Kuznetsov,S.A., Mankani,M., Robey,P.G., Poole,A.R., Pidoux,I.
et al. (1999) MT1-MMP-deficient mice develop dwarfism, osteope-
nia, arthritis, and connective tissue disease due to inadequate
collagen turnover. Cell, 99, 81–92.
8. Haass,C. and De Strooper,B. (1999) The presenilins in Alzheimer’s
disease—proteolysis holds the key. Science, 286, 916–919.
9. Peri,S., Navarro,J.D., Amanchy,R., Kristiansen,T.Z.,
Jonnalagadda,C.K., Surendranath,V., Niranjan,V., Muthusamy,B.,
Gandhi,T.K., Gronborg,M. et al. (2003) Development of human
protein reference database as an initial platform for approaching
systems biology in humans. Genome Res., 13, 2363–2371.
10. Apweiler,R., Bairoch,A., Wu,C.H., Barker,W.C., Boeckmann,B.,
Ferro,S., Gasteiger,E., Huang,H., Lopez,R., Magrane,M. et al.
(2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids
Res., 32, D115–D119.
11. Rawlings,N.D., Tolle,D.P. and Barrett,A.J. (2004) MEROPS:
the peptidase database. Nucleic Acids Res., 32, D160–D164.
12. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J.
(1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–410.
13. Eddy,S.R. (1998) Profile hidden Markov models. Bioinformatics, 14,
14. Krogh,A., Larsson,B., von Heijne,G. and Sonnhammer,E.L. (2001)
Predicting transmembrane protein topology with a hidden Markov
model: application to complete genomes. J. Mol. Biol., 305,
15. Bendtsen,J.D., Nielsen,H., von Heijne,G. and Brunak,S. (2004)
Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol.,
16. Cuff,J.A. and Barton,G.J. (2000) Application of multiple sequence
alignment profiles to improve protein secondary structure predic-
tion. Proteins, 40, 502–511.
17. Lupas,A. (1996) Prediction and analysis of coiled-coil structures.
Methods Enzymol., 266, 513–525.
Nucleic Acids Research, 2009,Vol. 37,Database issueD617
18. Wootton,J.C. and Federhen,S. (1996) Analysis of compositionally Download full-text
biased regions in sequence databases. Methods Enzymol., 266,
19. Sali,A. and Blundell,T.L. (1993) Comparative protein modelling
by satisfaction of spatial restraints. J. Mol. Biol., 234, 779–815.
20. Labarga,A., Valentin,F., Anderson,M. and Lopez,R. (2007)
Web services at the European bioinformatics institute. Nucleic Acids
Res., 35, W6–W11.
21. Igarashi,Y., Eroshkin,A., Gramatikova,S., Gramatikoff,K.,
Zhang,Y., Smith,J.W., Osterman,A.L. and Godzik,A. (2007)
CutDB: a proteolytic event database. Nucleic Acids Res., 35,
22. Boyd,S.E., Pike,R.N., Rudy,G.B., Whisstock,J.C. and Garcia de la
Banda,M. (2005) PoPS: a computational tool for modeling and
predicting protease specificity. J. Bioinform. Comput. Biol.,
23. Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T.,
Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003)
Cytoscape: a software environment for integrated models of
biomolecular interaction networks. Genome Res., 13, 2498–2504.
24. Suzek,B.E., Huang,H., McGarvey,P., Mazumder,R. and Wu,C.H.
(2007) UniRef: comprehensive and non-redundant UniProt refer-
ence clusters. Bioinformatics, 23, 1282–1288.
25. Eyre,T.A., Ducluzeau,F., Sneddon,T.P., Povey,S., Bruford,E.A. and
Lush,M.J. (2006) The HUGO Gene Nomenclature Database, 2006
updates. Nucleic Acids Res., 34, D319–D321.
26. Vailaya,A., Bluvas,P., Kincaid,R., Kuchinsky,A., Creech,M. and
Adler,A. (2005) An architecture for biological information extrac-
tion and representation. Bioinformatics, 21, 430–438.
27. Blinov,M.L., Faeder,J.R., Goldstein,B. and Hlavacek,W.S. (2004)
BioNetGen: software for rule-based modeling of signal transduction
based on the interactions of molecular domains. Bioinformatics, 20,
28. Slepchenko,B.M., Schaff,J.C., Macara,I. and Loew,L.M. (2003)
Quantitative cell biology with the virtual cell. Trends Cell Biol., 13,
Nucleic Acids Research, 2009, Vol. 37, Databaseissue