CutDB: a proteolytic event database
Yoshinobu Igarashi, Alexey Eroshkin, Svetlana Gramatikova, Kosi Gramatikoff,
Ying Zhang, Jeffrey W. Smith, Andrei L. Osterman and Adam Godzik*
Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
Received August 15, 2006; Revised October 2, 2006; Accepted October 3, 2006
Beyond the well-known role of proteolytic machin-
ery in protein degradation and turnover, many
specialized proteases play a key role in various
regulatory processes. Thousands of highly specific
proteolytic events are associated with normal and
pathological conditions, including bacterial and
viral infections. However, the information about
individual proteolytic events is dispersed over
multiple publications and is not easily available for
large-scale analysis. CutDB is one of the first
systematic efforts to build an easily accessible
collection of documented proteolytic events for
natural proteins in vivo or in vitro. A CutDB entry
is defined by a unique combination of these three
attributes: protease, protein substrate and cleavage
site. Currently, CutDB integrates 3070 proteolytic
events for 470 different proteases captured from
public archives (such as MEROPS and HPRD) and
publications. CutDB supports various types of
data searches and displays, including clickable
network diagrams. Most importantly, CutDB is a
Wikipedia approach, providing a convenient user
interface to input new data online. A recent con-
tribution of 568 proteolytic events by several experts
in the field of matrix metallopeptidases suggests
that this approach will significantly accelerate the
development of CutDB content. CutDB is publicly
available at http://cutdb.burnham.org.
Proteases degrade substrate proteins by cleaving peptide
bonds. Many proteases are highly specific and cleave sub-
strates only at specific sequence motifs. These proteases are
responsible not only for degrading proteins but also for
their activation/inactivation. Such proteolytic events (PEs)
form highly organized and regulated networks. However, a
comprehensive overview of proteolytic pathways has not
yet been elucidated.
PEs are involved in multiple aspects of regulation in
eukaryotic cells and play a major role in many natural
processes, as well as in many diseases, including cancer,
autoimmune diseases and bacterial and viral infections
(1–3), and are subjects of intensive research. However, at pre-
sent, the information about specific PEs is dispersed among
original articles and is not organized in a systematic manner,
such as information ‘metabolic pathways’ (4).
Several databases [e.g. MEROPS (5), HPRD (Human
Protein Reference Database) (6) and UniProt (7)] contain
some information about PEs; however, they are not a major
focus of any of them. MEROPS is a database for classifying
proteases and identifying proteases with tools, such as
BLAST, synonymous names search, protease inhibitor
information, comparative genome analysis tools and so on.
It contains information on some PEs, usually in text form.
MEROPS classification is a ‘gold standard’ in the protease
world, but extracting information about specific PEs requires
reading the text entries. HPRD and UniProt have a limited
number of records of PEs in their entries. However, none
of them is comprehensive enough to be used as a reference.
Also, none of them is designed to easily accept annotations
from a user community.
At present, most biological databases allow users to
contribute to them only by using e-mail or feedback forms.
Only recently some databases introduced a model of
community annotation, with interfaces for users to directly
edit their content [SEED (8) and VMD (9)]. This introduced
a new paradigm in distributed annotations that matches the
distribution of knowledge and expertise in the broad user
CutDB is a newly created community annotation database
of PEs that ultimately aims to reconstruct all proteolytic
pathways in their broad biological context. It was designed
to store PEs reported in original, experimental articles and
provide the PE data in a form accessible for large-scale
bioinformatics analyses, but also for individual searches by
experimental researchers. The content in CutDB is open to
the public, and it can be edited both in structured fields and
in the comments section, where users can express their
opinions by using free text. Thus, this database has the poten-
tial to offer not only a comprehensive overview of proteolytic
pathways but also the information that cannot be covered
in the framework of the database, such as hypotheses,
*To whom correspondence should be addressed. Tel: +1 858 646 3168; Fax: +1 858 713 9949; Email: email@example.com
? 2006 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.
Nucleic Acids Research, 2007, Vol. 35, Database issuePublished online 16 November 2006
discussions of discrepancies in experimental results, negative
results of experiments and so on.
The knowledge and information collected in CutDB will
allow us to improve our understanding of proteases and the
role of proteolysis in many important biological processes.
CutDB is one of the first systematic efforts to collect infor-
mation about PEs across all species, including humans.
CutDB is a part of PMAP (http://pmap.burnham.org), an
interactive in silico environment for interrogating data on
cellular proteolytic pathways which is being developed at
the NIH Roadmap Center for Proteolytic Pathways (see
CutDB contains three primary sections (Figure 1c): PE,
biological context and comments. A single PE record consists
of three basic elements: a protease, a substrate (protein) and a
unique cut site (cleavage site). The protease name and its
classification follow the MEROPS rules. The protease data
consist of a MEROPS protease definition, sequence code,
peptidase code and organism name. The substrate data are
mainly based on NCBI RefSeq data (10) and consist of the
NCBI GI sequence number, RefSeq definition, amino acid
sequence, organism name, cut site and size of products.
The cut site data consist of the position number in the
sequence and the eight amino acid residues around the cut
site. The biological context consist of biological consequen-
ces, pathway, cellular localization, tissue specificity, cell
line used in an experiment, disease, method of determination
and PubMed ID (Table 1). CutDB allows redundant records
in the case of multiple substrate names or substrate isoforms.
All other information that is not covered within designated
structured fields can be put in the comment section as free
text. The comment section is divided into several categories,
such as ‘discussion’, ‘hypothesis’, ‘drugs in development’
and ‘other comment’. The user can edit all this information,
including the PE and biological context sections. The user
can also delete a specific entry.
Currently, the total number of PEs in CutDB is 3070
(Table 2). We specifically focused on collecting information
about matrix metallopeptidase PEs from the articles reporting
original experiments (Table 3).
All protease and MEROPS peptidase codes are linked to the
MEROPS database. All substrates are linked to the NCBI
site. Links to UniProt, SEED and PubMed data are also
provided in the PE records.
Users can access each PE record from the front page by
using the pull-down menu or the text search (Figure 1a).
The pull-down menu directly displays the content of the
field in CutDB. The text search provides not only the fields
prepared in the pull-down menu but also the following
additional fields: cut site, creator name, last editor name
and PubMed ID. Both searches from the pull-down menu
and the text search return the set of PE records, which contain
Figure 1. (a) Front page of CutDB. (b) Search result using ‘M10.001 (matrix metallopeptidase-1)’ of MEROPS peptidase code. (c) Record describing ‘M10.001’
cleaves matrix metallopeptidase-1 preproprotein. (d) Network diagram centered around ‘M10.001’. (e) Structures of matrix metallopeptidase-1 in PDB with
highlighted cut sites.
Nucleic Acids Research, 2007, Vol. 35, Database issueD547
the query keywords in each record (Figure 1b). On the list
page, some records have links to their structure that show
the highlighted cut sites (Figure 1e). More detailed informa-
tion can be displayed by clicking ‘Detail’ on the right-hand
side of each record.
On the list page, users can generate a clickable network
diagram (Figure 1d) based on the listed PE records. At
present, only human PEs can be converted into the network
diagram. In the network diagram, the nodes correspond to
proteases and substrates. The proteases are shown as ellipses
and the substrates are shown as rectangles. The substrate
names are converted into HUGO’s gene symbols (11),
which are stored in NCBI RefSeq records. By using the
gene symbols, the network diagrams can be simplified by
shortening the substrate definitions and converting the multi-
ple substrate isoforms into one substrate symbol. The edges
correspond to the relation between proteases and substrates.
The colors of the edges show biological consequence: red
for substrate activation and blue for substrate inactivation.
Multiple edges are created in the case of multiple cut sites
for the same substrate.
Input assistant is an option that allows the user to quickly add
and edit the CutDB content. The user time for searching the
substrate sequence and GI number is minimized (Figure 2).
The information can be readily displayed without the need
to search the NCBI site. The user can also alternatively
enter the information manually into CutDB.
POSITION IDENTIFICATION TOOL
By using the position identification tool, the user can easily
identify both the position numbers and the eight amino acid
residues around cut sites. The user can access this interface
by clicking ‘tool’ when adding or editing the record. The
user has to copy and paste the sequence, put ‘j’ in the cut
site and click the ‘split’ button. As a result, the position
numbers and amino acid residues of cut sites appear.
Currently, the CutDB management system allows users to
access the database without registration. User registration is
necessary for adding and editing content. It is not necessary
to register to see or retrieve content.
Table 1. Objects and their attributes in the CutDB record
Name, organism, IDs
Name, organism, IDs, sequence, cut site, size of
Consequences (e.g. activation/inactivation),
pathway, tissue, cell line, cellular localization,
Confirmed in experiments or predicted, methods
of identification, original citation, curator,
curated date, comments
Table 2. Current number of proteolytic event records in CutDB
Protease family Number of records
Table 3. Number of proteolytic events in major metallopeptidases
Protease name Peptidase code by MEROPSNumber of records
Figure 2. (a) When the user inputs ‘cadhe’ into substrate definition, every
human protein definition in RefSeq that contains ‘cadhe’ in the definition is
immediately displayed in the pull-down menu. (b) When the user clicks one
of the protein names, the server starts to search protein names and returns the
chosen protein’s organism name to the next item on the Web browser. (c)
When the user selects ‘Homo sapiens’ from the organism names, the NCBI
GI number that contains both the protein and organism names is displayed in
the next pull-down menu. (d) The user selects one of the NCBI GI numbers;
then, its amino acid sequence and gene symbol appear.
D548Nucleic Acids Research, 2007, Vol. 35, Database issue
LITERATURE TRACK Download full-text
Users can access Literature Track (LT) by clicking
‘Literature Track’ on the front page. LT is a literature data-
base that stores two kinds of information: (i) information
from the articles that are already curated and/or contain no
cut site information or have only synthetic peptide infor-
mation; and (ii) the set of candidate articles that will be
read by curators in the near future. We intend to use LT to
manage the articles and avoid re-reading identical articles
by other curators. We will also use the data to build an
automatic system to filter appropriate articles from PubMed.
Only registered users can edit the content of LT.
DISCUSSION AND FUTURE DIRECTIONS
CutDB is a unique database that focuses on the relations
between proteases and their substrates. Our ultimate goal
is to collect a complete dataset on PEs in the cell and to
reconstruct all the proteolytic pathways in a broad biological
context. Currently, we have accumulated mainly PEs
involving metallopeptidases in human cells.
The CutDB information can be expanded by the imple-
mentation of additional networks of molecular interactions,
such as transcriptional regulation and protein–protein interac-
tions. We plan to import and implement these data, most of
which are publicly available, into CutDB in order to provide
integrated proteolytic pathways.
In addition, much research is being undertaken to imple-
ment automatic annotation approaches. Although complete
automatic annotation from literature is impossible as yet, it
is helpful for the curated database. The automatic annotation
system for CutDB will be based on coordination with the
Literature Track. We are currently engaging in novel
algorithmic/semiautomatic approaches to add content to
CutDB based on literature text-mining methods. Still, most
of the information is added by hand and current CutDB
contributors are members of several laboratories actively
working in the field of proteolysis and focusing on a limited
set of human proteases. The content of CutDB is expanding
rapidly; however, a much more extensive community effort
will be required to achieve more comprehensive coverage
of existing and rapidly growing knowledge about PEs in a
variety of species.
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 Graphviz. The protein structure is displayed
using Jmol. The information processing to transfer data into
MySQL was performed using BioRuby.
We extend our thanks to Dr Nobuya Tanaka for introducing
Ruby on Rails and his help in the early stages. We would also
like to thank Prof. Iris Lindberg, Prof. Alex Bateman and
Ms Olivia Haggis for their suggestions and sending us their
proteolytic event data. Finally, we thank Dr Boris Ratnikov
and many others at the Burnham Institute for Medical
Research for curating the data. This research is funded by
NIH grant number 5 U54 RR020843-03, ‘Center on
Proteolytic Pathways’. Funding to pay the Open Access
publication charges for this article was provided by NIH/
Burnham Institute for Medical Research.
Conflict of interest statement. None declared.
1. Nagase,H. and Woessner,J.F., Jr (1999) Matrix metalloproteinases.
J. Biol. Chem., 274, 21491–21494.
2. Fuentes-Prior,P. and Salvesen,G.S. (2004) The protein structures that
shape caspase activity, specificity, activation and inhibition. Biochem.
J., 384, 201–232.
3. Strongin,A.Y. (2006) Mislocalization and unconventional functions of
cellular MMPs in cancer. Cancer Metastasis Rev., 25, 87–98.
4. Kanehisa,M., Goto,S., Hattori,M., Aoki-Kinoshita,K.F., Itoh,M.,
Kawashima,S., Katayama,T., Araki,M. and Hirakawa,M. (2006) From
genomics to chemical genomics: new developments in KEGG. Nucleic
Acids Res., 34, D354–D357.
5. Rawlings,N.D., Tolle,D.P. and Barrett,A.J. (2004) MEROPS: the
peptidase database. Nucleic Acids Res., 32, D160–D164.
6. 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.
7. 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,
8. Overbeek,R., Begley,T., Butler,R.M., Choudhuri,J.V., Chuang,H.Y.,
Cohoon,M., de Crecy-Lagard,V., Diaz,N., Disz,T., Edwards,R. et al.
(2005) The subsystems approach to genome annotation and its use in
the project to annotate 1000 genomes. Nucleic Acids Res., 33,
9. Tripathy,S., Pandey,V.N., Fang,B., Salas,F. and Tyler,B.M. (2006)
VMD: a community annotation database for oomycetes and microbial
genomes. Nucleic Acids Res., 34, D379–D381.
10. Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2005) NCBI Reference
Sequence (RefSeq): a curated non-redundant sequence database
of genomes, transcripts and proteins. Nucleic Acids Res., 33,
11. 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.
Nucleic Acids Research, 2007, Vol. 35, Database issueD549