TOPSAN: a dynamic web database for
structural genomics
Kyle Ellrott1,2, Christian M. Zmasek3,4, Dana Weekes1,4, S. Sri Krishna2,4,
Constantina Bakolitsa1,4, Adam Godzik1,2,3,4,* and John Wooley1,2,4,*
1Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute,
10901 North Torrey Pines Road, La Jolla, CA 92037, 2Center for Research in Biological Systems, University of
California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, 3Joint Center for Molecular Modeling and
4Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
Received August 16, 2010; Revised September 21, 2010; Accepted September 22, 2010
ABSTRACT
The Open Protein Structure Annotation Network
(TOPSAN) is a web-based collaboration platform
for exploring and annotating structures determined
by structural genomics efforts. Characterization of
those structures presents a challenge since the
majority of the proteins themselves have not yet
been characterized. Responding to this challenge,
the TOPSAN platform facilitates collaborative anno-
tation and investigation via a user-friendly web-
based interface pre-populated with automatically
generated information. Semantic web technologies
expand and enrich TOPSAN’s content through links
to larger sets of related databases, and thus, enable
data integration from disparate sources and data
mining via conventional query languages. TOPSAN
can be found at http://www.topsan.org.
INTRODUCTION
Over the past decade, structural genomics (SG) efforts in
the USA alone have determined the structures of more
than 3000 previously uncharacterized proteins at a sus-
tained rate of over 500 novel structure depositions per
year to the Protein Databank (PDB) (1). Through the dis-
covery of numerous new folds and an even greater number
of variants of known folds (2), SG structures provide key
input for innovative research into protein evolution and
function. One of the main challenges presented by such
high-throughput research involves the timely annotation
and integration of the resulting data to provide direct
input into ongoing research within the greater biological
community. Traditional mechanisms for publication
are simply too slow to keep pace with the speed of
structure determination. Thus, currently over 90% of
SG deposited structures are not yet described in literature.
The rate and volume of protein structures being produced
requires novel mechanisms to ensure that the knowledge
gained by these structures is disseminated in a timely
manner.
Several new protein structure annotation platforms,
using wiki-based methods, have been described (3–5).
However, their content is largely static and derived from
peer-reviewed publications, aspects that do not easily lend
themselves to exploring new knowledge about structures.
We developed The Open Protein Structure Annotation
Network (TOPSAN) to serve both as an annotation and
a communication platform with the goal of facilitating
and accelerating research relevant to SG structures.
TOPSAN integrates a wide range of information about
SG proteins, from different high-throughput experiments
to literature, evolutionary analysis and even functional
predictions. Through the implementation of a semantic
web layer in the current version, TOPSAN enables
database-like searches through its entire content and
thus promotes further integration between its content
and mainstream biology.
THE DATABASE
Content and interface
TOPSAN currently contains annotations for over 7250
structures from SG efforts from around the world.
Prominent among these are several hundred structures
that represent the first experimentally characterized
members of their respective families, as well as many
proteins for which there is extensive interest within the
research community. Annotations and collaborations de-
veloped via TOPSAN have led to peer-reviewed publica-
tions for several dozen proteins, with many more currently
*To whom correspondence should be addressed. Tel: 858 646 3168; Fax: 858 795 5249; Email: adam@sanfordburnham.org
Correspondence may also be addressed to John Wooley. Tel: 858 534 2494; Fax: 858 822 1452; Email: jwooley@ucsd.edu
D494–D496 Nucleic Acids Research, 2011, Vol. 39, Database issue Published online 20 October 2010
doi:10.1093/nar/gkq902
 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/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

in different stages of development. An overview of the
database interface is given in Figure 1.
Implementation
Implementation of the TOPSAN platform has been
described in detail elsewhere (6). In brief, TOPSAN was
developed using MindTouch, an enterprise open source
collaboration and integration platform, which provides
tools and scripting capabilities that were used to develop
a dynamic website. At the backend, data are collected
from a variety of different sources, using multiple tools
that have been integrated into the MindTouch platform.
An application called TopsanApp is used to retrieve
Figure 1. Screenshot of a TOPSAN entry [PDB id: 3kk7 (9)]. Automatically generated data (data from external sources) are combined with human
input (WYSIWIG editor, tagging, discussion page) and analyses (tools). Authorship and version tracking enable accountability and quality control.
Semantic web technologies enable easy import and export of this data. Details of the data specifications can be found on the website.
Nucleic Acids Research, 2011, Vol. 39, Database issue D495

protein information from external resources and to create
and store pages for specified proteins on the platform via
an API. Data collected by TopsanApp are stored in a local
MySQL database (termed topsanDB) that is used to
generate individual protein pages with built-in functions
for easy access and manipulation of information.
TOPSAN additionally utilizes a semantic web-based
data import system that enables rapid integration of new
data. Semantic web is an architectural layer built on top of
existing web pages that consists of hidden embedded tags
that employ standardized ontology, allowing searches
normally associated with structured databases to be
carried out across unstructured data collections (7,8).
For our purposes, the semantic web provides a unified
framework for integration of data automatically
imported from external sources and human-curated anno-
tations. In this environment, scripting calls made from the
Dekiscript environment build requests to access and
convert XML formatted data available on the web into
a semantic web compatible format. Data from compatible
sites that provide all records in a semantic web compatible
format can be imported directly with no manual conver-
sion. Thus, semantic web data can be automatically
imported to TOPSAN from Pfam, UniProt, KEGG
Pathway database and PDB. Other sources of data can
be imported with a variety of modular, easily adapted
plug-ins. Once imported, the data can be queried and
utilized in the same web-based Dekiscript environment
that was used for the import. Data export is handled by
a variety of modular tools that make data available in
formats including standard HTML, stripped-down
XML, RDF/XML and RDFa. In addition to individual
protein annotations, bulk compilations of the entire site
are available as compressed files. TOPSAN also provides
embeddable web interfaces to help other websites, such as
Pfam, integrate TOPSAN annotations. Annotations can
be viewed by anyone, but only registered users are enabled
to contribute text. Accountability and ownership of ideas
is preserved via time-stamped tracking of contributions.
Conclusions and future perspectives
TOPSAN explores an important nexus between human
analysis and computational data mining, neither of
which can independently handle the challenges of
research in a high-throughput data generation era.
Future TOPSAN improvements include developing
statistical methods for determining the reliability of
information extracted from databases and testing means
for improving semantic functionality. Additionally,
TOPSAN emphasizes user-friendly access to external
resources and databases, which might otherwise be
unknown or not easy to access for some users.
Javascript-based widgets allow users to view and edit an-
notations while capturing and storing their input in a
format that is compatible with the semantic web.
ACKNOWLEDGEMENTS
The content is solely the responsibility of the authors and
does not necessarily represent the official views of the
National Institute of General Medical Sciences or the
National Institutes of Health.
FUNDING
National Institutes of Health National Institute of
General Medical Sciences Protein Structure Initiative
(grant No. U54 GM074898). Funding for open access
charge: University of California San Diego.
Conflict of interest statement. None declared.
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