PROFESS: a PROtein Function, Evolution, Structure and Sequence database
Database the journal of biological databases and curation (2010)
- DOI: 10.1093/database/baq011
- PubMed: 20624718
Available from
Thomas Triplet and Jaime Stark's profiles on Mendeley.
or
Abstract
The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of proteinprotein interaction networks. Database URL: http://cse.unl.edu/profess/
Available from
Thomas Triplet and Jaime Stark's profiles on Mendeley.
Page 1
PROFESS: a PROtein Function, Evolution, Structure and Sequence database
Original article
PROFESS: a PROtein Function, Evolution,
Structure and Sequence database
Thomas Triplet1,†, Matthew D. Shortridge2, Mark A. Griep2, Jaime L. Stark2,
Robert Powers2,* and Peter Revesz1,*
1Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0115 and 2Department of Chemistry,
University of Nebraska-Lincoln, Lincoln NE 68588-0304, USA
*Corresponding author: Tel: +1 402 472 3039; Fax: +1 402 472 9402; Email: rpowers3@unl.edu
*Correspondence may also be addressed to Peter Revesz. Tel: +1 402 472 3488; Fax: +1 402 472 7767; Email: revesz@cse.unl.edu
†Present address: Thomas Triplet, Department of Computer Science, Concordia University, Montreal, Qc H3G-1M8, Canada.
Submitted 2 December 2009; Revised 3 June 2010; Accepted 6 June 2010
.............................................................................................................................................................................................................................................................................................
The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on
biological sciences and transforming the way research is conducted. There are 1100 molecular biology databases dis-
persed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of
novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function,
Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine
analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an
intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not
conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural
drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the
observation of protein–protein interaction networks.
Database URL: http://cse.unl.edu/profess/
.............................................................................................................................................................................................................................................................................................
Introduction
There are 1100 molecular biology databases freely avail-
able to the public online (1,2). These databases constitute
the extent of our knowledge related to genomics, prote-
omics, metabolomics, and structural genomics. Most serve
as data warehouses with simple interfaces for data retrieval
(3). To address more complex questions, biologists are rou-
tinely required to develop new databases by filtering infor-
mation from existing databases (4). Even though this is
extremely inefficient, there are a growing number of
specialized databases designed around single topics.
Unfortunately, this simply propagates the underlying prob-
lem: an inability to utilize the data outside the constraints
imposed by the database designers (5). Capitalizing on the
potential of biological information requires the develop-
ment of a next-generation database that enables biologists
to explore biological data in new ways. The key to solving
this problem is to move the design focus from the database
structure (predefined relationships between fields) to a
fluid association that can be adapted to a biologist’s ques-
tions (6) without re-designing the underlying data struc-
ture. However, there are barriers to linking individual
databases because of different data formats and structure
(7, 8). Thus, it was essential to this effort to implement a
new approach to integrate diverse biological databases (9).
Most of the work on database integration has focused
on business and spatio-temporal data (10, 11). Satisfying,
general and practical solutions have proven to be elusive
for these complex data sources, which are actually simple
compared to biological data. Nevertheless, the most versa-
tile of the solutions is to use a separate adapter, or ‘wrap-
per’ (Figure 1), program around each source database (12).
The ‘wrappers’ provide a simplified ‘view’ of the source
.............................................................................................................................................................................................................................................................................................
The Author(s) 2010. Published by Oxford University Press.
This is 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. Page 1 of 11
(page number not for citation purposes)
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
PROFESS: a PROtein Function, Evolution,
Structure and Sequence database
Thomas Triplet1,†, Matthew D. Shortridge2, Mark A. Griep2, Jaime L. Stark2,
Robert Powers2,* and Peter Revesz1,*
1Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0115 and 2Department of Chemistry,
University of Nebraska-Lincoln, Lincoln NE 68588-0304, USA
*Corresponding author: Tel: +1 402 472 3039; Fax: +1 402 472 9402; Email: rpowers3@unl.edu
*Correspondence may also be addressed to Peter Revesz. Tel: +1 402 472 3488; Fax: +1 402 472 7767; Email: revesz@cse.unl.edu
†Present address: Thomas Triplet, Department of Computer Science, Concordia University, Montreal, Qc H3G-1M8, Canada.
Submitted 2 December 2009; Revised 3 June 2010; Accepted 6 June 2010
.............................................................................................................................................................................................................................................................................................
The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on
biological sciences and transforming the way research is conducted. There are 1100 molecular biology databases dis-
persed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of
novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function,
Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine
analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an
intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not
conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural
drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the
observation of protein–protein interaction networks.
Database URL: http://cse.unl.edu/profess/
.............................................................................................................................................................................................................................................................................................
Introduction
There are 1100 molecular biology databases freely avail-
able to the public online (1,2). These databases constitute
the extent of our knowledge related to genomics, prote-
omics, metabolomics, and structural genomics. Most serve
as data warehouses with simple interfaces for data retrieval
(3). To address more complex questions, biologists are rou-
tinely required to develop new databases by filtering infor-
mation from existing databases (4). Even though this is
extremely inefficient, there are a growing number of
specialized databases designed around single topics.
Unfortunately, this simply propagates the underlying prob-
lem: an inability to utilize the data outside the constraints
imposed by the database designers (5). Capitalizing on the
potential of biological information requires the develop-
ment of a next-generation database that enables biologists
to explore biological data in new ways. The key to solving
this problem is to move the design focus from the database
structure (predefined relationships between fields) to a
fluid association that can be adapted to a biologist’s ques-
tions (6) without re-designing the underlying data struc-
ture. However, there are barriers to linking individual
databases because of different data formats and structure
(7, 8). Thus, it was essential to this effort to implement a
new approach to integrate diverse biological databases (9).
Most of the work on database integration has focused
on business and spatio-temporal data (10, 11). Satisfying,
general and practical solutions have proven to be elusive
for these complex data sources, which are actually simple
compared to biological data. Nevertheless, the most versa-
tile of the solutions is to use a separate adapter, or ‘wrap-
per’ (Figure 1), program around each source database (12).
The ‘wrappers’ provide a simplified ‘view’ of the source
.............................................................................................................................................................................................................................................................................................
The Author(s) 2010. Published by Oxford University Press.
This is 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. Page 1 of 11
(page number not for citation purposes)
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 2
database presented in a form that is easier-to-use than the
original source database. In fact, some parts of the source
data may be completely omitted in this repacked presenta-
tion, leaving only the parts of the data that are needed for
the enterprise that wants to use it. The advantage of the
‘answering queries using views’ approach to the database
integration problem is that it reduces the integration prob-
lem to two steps: (i) building wrappers of the source data-
bases, thereby providing simple ‘views’, and (ii) applying
standard database queries on the views. Thus, implement-
ing wrappers enables a robust query system that incorpor-
ates a variety of similarity functions capable of generating
data relationships not conceived during the creation of the
database. This will allow the user to move beyond simple
text-based queries. Therefore, the PROFESS (PROtein
Function, Evolution, Structure and Sequence) database
uses wrappers to assist in the structural, functional and evo-
lutionary analysis of the abundant number of novel pro-
teins continually identified from whole-genome
sequencing.
Database content
Fourteen sources of data were integrated to create
PROFESS (Table 1) using a local-as-view (LAV) modular ap-
proach (Figure 1B) (see the ‘Method for data integration’
section for details). The modular functionality of PROFESS
coupled with user friendly searching capabilities makes
PROFESS particularly useful for asking a range of questions
about the sequence, structure, and functional relationship
of evolutionary and functionally related proteins. A user
interacts with PROFESS through a web interface using a
functional-style query language that is translated to the
structure query language (SQL) for mining PROFESS
(Figure 2A). The core of PROFESS established a relationship
between the Protein Data Bank (PDB) (13) and the eggNOG
databases (14, 15) (Figure 2B). The link between eggNOG
with the PDB was established using the proteins UniProt
accession numbers and the UniProt Mapping service (16).
To simplify the interface, each orthologous protein
family has four tabs containing information about: func-
tion, evolution, structure and sequence. An additional
tab, diseases, shows linkages between human proteins
and information culled from databases devoted to the
functional genomics and proteomics of particular diseases.
Each protein is annotated with its source organism using
the UniProtKB taxonomy database (16). Each level of the
PROFESS database mines pieces of information from all the
integrated databases and provides the user with compre-
hensive tables highlighting annotations (Figure 3). The
tables are defined as independent modules, each providing
a unique representation of the integrated data. Each
module can be activated or deactivated, depending on
the specific needs of the user. PROFESS is not limited in
the size or type of data that can be incorporated due to
the LAV approach coupled with a modular interface. This
allows the integration of biological data for rapid identifi-
cation of biologically relevant similarities or differences be-
tween various protein functions.
Function
The Function tab of PROFESS summarizes the biological
function of an orthologous cluster. For three primary de-
scriptions of protein function, the numbers of proteins
within each class (within the current orthologous cluster)
are computed and the distributions are represented as pie
charts. This allows the user to quickly differentiate relevant
classes from outliers. Classes are sorted by decreasing
number of proteins. The darker the color in the pie chart,
the higher the number of proteins. As an example a search
of ‘collagenase’ retrieved 34 different orthologous groups,
one group (prNOG04586) is shown in Figure 3.
Figure 1. Two solutions for the data integration problem. (A) The ETL software extracts, transforms and loads the data sources
into the warehouse. (B) The more flexible local-as-view method defines a virtual database that interacts with data sources
through wrappers, which provide simplified views of the original databases.
.............................................................................................................................................................................................................................................................................................
Page 2 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
original source database. In fact, some parts of the source
data may be completely omitted in this repacked presenta-
tion, leaving only the parts of the data that are needed for
the enterprise that wants to use it. The advantage of the
‘answering queries using views’ approach to the database
integration problem is that it reduces the integration prob-
lem to two steps: (i) building wrappers of the source data-
bases, thereby providing simple ‘views’, and (ii) applying
standard database queries on the views. Thus, implement-
ing wrappers enables a robust query system that incorpor-
ates a variety of similarity functions capable of generating
data relationships not conceived during the creation of the
database. This will allow the user to move beyond simple
text-based queries. Therefore, the PROFESS (PROtein
Function, Evolution, Structure and Sequence) database
uses wrappers to assist in the structural, functional and evo-
lutionary analysis of the abundant number of novel pro-
teins continually identified from whole-genome
sequencing.
Database content
Fourteen sources of data were integrated to create
PROFESS (Table 1) using a local-as-view (LAV) modular ap-
proach (Figure 1B) (see the ‘Method for data integration’
section for details). The modular functionality of PROFESS
coupled with user friendly searching capabilities makes
PROFESS particularly useful for asking a range of questions
about the sequence, structure, and functional relationship
of evolutionary and functionally related proteins. A user
interacts with PROFESS through a web interface using a
functional-style query language that is translated to the
structure query language (SQL) for mining PROFESS
(Figure 2A). The core of PROFESS established a relationship
between the Protein Data Bank (PDB) (13) and the eggNOG
databases (14, 15) (Figure 2B). The link between eggNOG
with the PDB was established using the proteins UniProt
accession numbers and the UniProt Mapping service (16).
To simplify the interface, each orthologous protein
family has four tabs containing information about: func-
tion, evolution, structure and sequence. An additional
tab, diseases, shows linkages between human proteins
and information culled from databases devoted to the
functional genomics and proteomics of particular diseases.
Each protein is annotated with its source organism using
the UniProtKB taxonomy database (16). Each level of the
PROFESS database mines pieces of information from all the
integrated databases and provides the user with compre-
hensive tables highlighting annotations (Figure 3). The
tables are defined as independent modules, each providing
a unique representation of the integrated data. Each
module can be activated or deactivated, depending on
the specific needs of the user. PROFESS is not limited in
the size or type of data that can be incorporated due to
the LAV approach coupled with a modular interface. This
allows the integration of biological data for rapid identifi-
cation of biologically relevant similarities or differences be-
tween various protein functions.
Function
The Function tab of PROFESS summarizes the biological
function of an orthologous cluster. For three primary de-
scriptions of protein function, the numbers of proteins
within each class (within the current orthologous cluster)
are computed and the distributions are represented as pie
charts. This allows the user to quickly differentiate relevant
classes from outliers. Classes are sorted by decreasing
number of proteins. The darker the color in the pie chart,
the higher the number of proteins. As an example a search
of ‘collagenase’ retrieved 34 different orthologous groups,
one group (prNOG04586) is shown in Figure 3.
Figure 1. Two solutions for the data integration problem. (A) The ETL software extracts, transforms and loads the data sources
into the warehouse. (B) The more flexible local-as-view method defines a virtual database that interacts with data sources
through wrappers, which provide simplified views of the original databases.
.............................................................................................................................................................................................................................................................................................
Page 2 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 3
The Function tab also contains three unique sub-modules
that describe the primary biological function of a cluster of
orthologs. The first module, Functions, is a table of the
functional annotations for a protein structure taken from
the PDB, including the protein families (PFAM) (17), gene
ontology (GO) (18) and enzyme commission (EC) number
(19). It is left to the user to examine the combination of
annotations to assess its overall consistency and to identify
possible mis-annotations. Protein function can also be
described by protein interaction partners, therefore two
additional modules (ligands and protein interactions) list
the ligands and proteins experimentally shown to interact
with members of the eggNOG family. The Ligands module
displays details about ligands known to bind a protein
based on ligand bound structures in the PDB as well as
cross-references to the Kyoto Encyclopedia of genes and
Table 1. Core databases currently integrated in PROFESS
Name PROFESS level Link Reference
CATH database Structure http://www.cathdb.info/ (27)
eggNOG database Function http://eggnog.embl.de/ (15)
Enzyme classification Function http://www.chem.qmul.ac.uk/iubmb/enzyme/ (19)
Database of essential genes (DEG) Evolution http://www.essentialgene.org/ (26)
Database of interaction proteins (DIP) Function http://dip.doe-mbi.ucla.edu/ (22)
Orthologous structure and sequence-based phylogenies Evolution This database
Orthologous structure similarity comparisons Structure This database
Pancreatic cancer related proteins Disease This database
Gene ontology Function http://www.geneontology.org/ (18)
GenBank Sequence http://www.ncbi.nlm.nih.gov/Genbank/ (60)
KEGG ligands Function http://www.genome.jp/kegg/ligand.html (20)
Protein data bank (PDB) Structure http://www.rcsb.org/ (13)
Protein families (PFAM) database Function http://pfam.sanger.ac.uk/ (17)
Protein/protein interactions in E. coli Function http://genome.cshlp.org/content/16/5/686.abstract (21)
SCOP Structure http://www.bio.cam.ac.uk/scop/ (28)
Swiss-Prot Sequence http://www.uniprot.org/ (61)
TrEMBL Sequence http://www.uniprot.org/ (61)
UniProtKB taxonomy All http://www.uniprot.org/taxonomy/ (16)
Figure 2. Outline of the PROFESS database. (A) The relationship of the user interface to the functional query system (green) to
the PROFESS databases; and (B) the core databases integrated in PROFESS. The central eggNOG-PDB linkage is shown in red,
double arrows indicate intensive interactions, blue boxes represent databases available on the internet, and purple boxes denote
other databases to be integrated in the future. Each additional data set interacts with the PROFESS core through the use of
wrapper programs to make query language uniform.
.............................................................................................................................................................................................................................................................................................
Page 3 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
that describe the primary biological function of a cluster of
orthologs. The first module, Functions, is a table of the
functional annotations for a protein structure taken from
the PDB, including the protein families (PFAM) (17), gene
ontology (GO) (18) and enzyme commission (EC) number
(19). It is left to the user to examine the combination of
annotations to assess its overall consistency and to identify
possible mis-annotations. Protein function can also be
described by protein interaction partners, therefore two
additional modules (ligands and protein interactions) list
the ligands and proteins experimentally shown to interact
with members of the eggNOG family. The Ligands module
displays details about ligands known to bind a protein
based on ligand bound structures in the PDB as well as
cross-references to the Kyoto Encyclopedia of genes and
Table 1. Core databases currently integrated in PROFESS
Name PROFESS level Link Reference
CATH database Structure http://www.cathdb.info/ (27)
eggNOG database Function http://eggnog.embl.de/ (15)
Enzyme classification Function http://www.chem.qmul.ac.uk/iubmb/enzyme/ (19)
Database of essential genes (DEG) Evolution http://www.essentialgene.org/ (26)
Database of interaction proteins (DIP) Function http://dip.doe-mbi.ucla.edu/ (22)
Orthologous structure and sequence-based phylogenies Evolution This database
Orthologous structure similarity comparisons Structure This database
Pancreatic cancer related proteins Disease This database
Gene ontology Function http://www.geneontology.org/ (18)
GenBank Sequence http://www.ncbi.nlm.nih.gov/Genbank/ (60)
KEGG ligands Function http://www.genome.jp/kegg/ligand.html (20)
Protein data bank (PDB) Structure http://www.rcsb.org/ (13)
Protein families (PFAM) database Function http://pfam.sanger.ac.uk/ (17)
Protein/protein interactions in E. coli Function http://genome.cshlp.org/content/16/5/686.abstract (21)
SCOP Structure http://www.bio.cam.ac.uk/scop/ (28)
Swiss-Prot Sequence http://www.uniprot.org/ (61)
TrEMBL Sequence http://www.uniprot.org/ (61)
UniProtKB taxonomy All http://www.uniprot.org/taxonomy/ (16)
Figure 2. Outline of the PROFESS database. (A) The relationship of the user interface to the functional query system (green) to
the PROFESS databases; and (B) the core databases integrated in PROFESS. The central eggNOG-PDB linkage is shown in red,
double arrows indicate intensive interactions, blue boxes represent databases available on the internet, and purple boxes denote
other databases to be integrated in the future. Each additional data set interacts with the PROFESS core through the use of
wrapper programs to make query language uniform.
.............................................................................................................................................................................................................................................................................................
Page 3 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
Page 4
genomes (KEGG) (20). Common buffers, detergents, ions
and solvents are listed separately to provide rapid access
to biologically relevant data. The protein interactions
module lists protein interactions found in Escherichia coli
(21). The interactions were correlated to the corresponding
PDB ID by matching bait and prey genes to their represen-
tative eggNOG cluster. The protein interactions module
also integrates the 69 171 manually curated protein/protein
interactions (as of April 2010) in 274 organisms from the
database of interacting proteins (22).
Evolution
The Evolution tab of PROFESS displays a table of essen-
tial genes, along with sequence- and structure-based phylo-
genetic trees. The sequence tree shows the unrooted
phylogenetic tree created from the tree files downloaded
from the eggNOG database (14, 15). The final image was
generated using DrawTree from the Phylip package
(23, 24). The sequence trees contain many branches and
nodes and provide an overview of the overall bushy
nature of the cluster, a more detailed tree can be found
Figure 3. Screenshot of the result page for prNOG04586. A brief description of the cluster is displayed (top) along with statistics.
Detailed data is shown for each level (function, evolution, structure, sequence and disease). At each level, data is further
clustered into different modules, each module providing a unique view of the data. Each module may be activated or deacti-
vated depending on the needs of the user. The screenshot shows the module summarizing functional annotations of proteins in
prNOG04586. Data is mined from the enzyme classification, the protein families database and the gene ontology. For each
database, PROFESS shows entries related to proteins within cluster prNOG04586. The pie charts represent the relative frequency
of each database entry within the orthologous cluster.
.............................................................................................................................................................................................................................................................................................
Page 4 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
and solvents are listed separately to provide rapid access
to biologically relevant data. The protein interactions
module lists protein interactions found in Escherichia coli
(21). The interactions were correlated to the corresponding
PDB ID by matching bait and prey genes to their represen-
tative eggNOG cluster. The protein interactions module
also integrates the 69 171 manually curated protein/protein
interactions (as of April 2010) in 274 organisms from the
database of interacting proteins (22).
Evolution
The Evolution tab of PROFESS displays a table of essen-
tial genes, along with sequence- and structure-based phylo-
genetic trees. The sequence tree shows the unrooted
phylogenetic tree created from the tree files downloaded
from the eggNOG database (14, 15). The final image was
generated using DrawTree from the Phylip package
(23, 24). The sequence trees contain many branches and
nodes and provide an overview of the overall bushy
nature of the cluster, a more detailed tree can be found
Figure 3. Screenshot of the result page for prNOG04586. A brief description of the cluster is displayed (top) along with statistics.
Detailed data is shown for each level (function, evolution, structure, sequence and disease). At each level, data is further
clustered into different modules, each module providing a unique view of the data. Each module may be activated or deacti-
vated depending on the needs of the user. The screenshot shows the module summarizing functional annotations of proteins in
prNOG04586. Data is mined from the enzyme classification, the protein families database and the gene ontology. For each
database, PROFESS shows entries related to proteins within cluster prNOG04586. The pie charts represent the relative frequency
of each database entry within the orthologous cluster.
.............................................................................................................................................................................................................................................................................................
Page 4 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 5
by searching a particular cluster using the eggNOG data-
base (14, 15).
The structure tree shows the unrooted phylogenetic tree
generated using PDB protein structures. The structures
were aligned using MAMMOTH-mult (25) and the structure
based sequence alignment was used to compute the trees
and image. Branch lengths for each structure alignment
from MAMMOTH-mult (25) were measured by our in
house software and minimized using the neighbor joining
program implemented in Phylip (24). The final image was
generated in the same manner as the sequence tree.
The essential genes module of the evolution level shows
whether the protein in the orthologous cluster is essential
and was obtained from the database of essential genes
(DEG) (26). As of version 5.4, DEG includes 5260 essential
prokaryotic genes and 5040 eukaryotic genes extracted
from the literature. Genes are displayed with correspond-
ing protein structures from the PDB (see module Sequence
similarities for more details about the association gene/
structure). As with all databases, DEG should not be
viewed as an exhaustive or complete list of all essential
genes, but only as a work in progress. For instance,
well-established and obviously essential genes may not be
included in DEG, because its focus is on the current litera-
ture. Since PROFESS is continually updated and expanded,
the list of classified essential genes will continue to expand
as new studies are carried out and as DEG reaches deeper
into the older literature.
Structure
The structure tab of PROFESS contains all structures asso-
ciated with an eggNOG cluster and is linked together by
their Uniprot accession numbers. Therefore, the availability
of a structure in PROFESS is limited to a preexisting
Uniprot-eggNOG linkage. If a Uniprot-eggNOG linkage
does not exist for a queried structure, then the structure
is not present in PROFESS and will not be displayed in the
results summary. The structure tab also contains an aggre-
gate table of data from the CATH (27) and SCOP (28) data-
bases. Due to copyright restrictions, links are provided to
retrieve data from the SCOP website rather than reprodu-
cing SCOP data on our pages.
The structure tab is designed to ease searching for all
orthologous clusters with a particular fold. This is accom-
plished by either direct or iterative searching for a particu-
lar CATH ID number. The direct searching method would be
to enter a known CATH ID into the PROFESSor to find the
correlated orthologous clusters. In iterative searching,
a user first searches for a protein structure with the
PROFESSor to identify the orthologous group, finds the
CATH ID in the structure tab, and then searches the selected
CATH ID with the PROFESSor. Both searching methods will
generate a list of orthologous clusters that contain the pro-
tein fold of interest.
The structure level also contains all pairwise structure
alignments of an orthologous cluster. The pairwise struc-
ture comparison tool DaliLite (29, 30) was used to measure
the backbone structure similarity of proteins within each
orthologous cluster defined by the eggNOG database.
All-against-all pairwise structural comparisons were carried
out for all 224 847 NOGs with 401 967 total structure com-
parisons. Structure calculations were completed with help
from the Holland Computing Center of the University of
Nebraska-Lincoln.
The Dali Z-scores were normalized to calculate a fraction-
al structure similarity (FSS) score: FSS = ZAB/ZAA, where ZAB is
the Dali Z-score when protein B is compared to protein A
and ZAA is the Z-score when protein A is compared to itself.
Thus, ZAA represents the maximum Z-score that can be
achieved for perfect similarity. FSS provides a simple nor-
malized and quantitative measure of the distance the two
proteins have diverged in their structures.
Sequence
The sequence tab of PROFESS lists all protein sequences
within the orthologous cluster. The sequence tab also pro-
vides the Uniprot accession numbers, molecular weight,
length of sequence and when available the structure.
A list of all sequences from each orthologous group is
downloadable into FASTA format and each sequence can
be individually copied and pasted into a text document in
FASTA format.
Diseases
The diseases tab of PROFESS is reserved for gene and pro-
tein information identified throughout the literature as
being involved in various human diseases. Currently,
PROFESS includes information about genes and proteins
involved in pancreatic cancer but this level of PROFESS
will grow rapidly as new data is incorporated.
Query system for data mining
The PROFESSor
The primary search function of PROFESS is the PROFESSor
(Figure 4), a unified text field that will assist the user to
easily refine complex queries by dynamically suggesting
entries from any integrated database. The PROFESSor as-
sists the user by correcting for spelling errors using
Levenshtein metrics, as well as providing a user defined
focused browsing feature. For instance, upon typing in
the query ‘collagenase’, the PROFESSor returns a drop
down list of protein folds and functions that have known
relation with collagenase (Figure 4). If a user selects the fold
(CATH) suggestion, PROFESS will return all functional clus-
ters known to contain that fold. The PROFESSor searches
all other data sources within PROFESS in the same manner.
.............................................................................................................................................................................................................................................................................................
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Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
base (14, 15).
The structure tree shows the unrooted phylogenetic tree
generated using PDB protein structures. The structures
were aligned using MAMMOTH-mult (25) and the structure
based sequence alignment was used to compute the trees
and image. Branch lengths for each structure alignment
from MAMMOTH-mult (25) were measured by our in
house software and minimized using the neighbor joining
program implemented in Phylip (24). The final image was
generated in the same manner as the sequence tree.
The essential genes module of the evolution level shows
whether the protein in the orthologous cluster is essential
and was obtained from the database of essential genes
(DEG) (26). As of version 5.4, DEG includes 5260 essential
prokaryotic genes and 5040 eukaryotic genes extracted
from the literature. Genes are displayed with correspond-
ing protein structures from the PDB (see module Sequence
similarities for more details about the association gene/
structure). As with all databases, DEG should not be
viewed as an exhaustive or complete list of all essential
genes, but only as a work in progress. For instance,
well-established and obviously essential genes may not be
included in DEG, because its focus is on the current litera-
ture. Since PROFESS is continually updated and expanded,
the list of classified essential genes will continue to expand
as new studies are carried out and as DEG reaches deeper
into the older literature.
Structure
The structure tab of PROFESS contains all structures asso-
ciated with an eggNOG cluster and is linked together by
their Uniprot accession numbers. Therefore, the availability
of a structure in PROFESS is limited to a preexisting
Uniprot-eggNOG linkage. If a Uniprot-eggNOG linkage
does not exist for a queried structure, then the structure
is not present in PROFESS and will not be displayed in the
results summary. The structure tab also contains an aggre-
gate table of data from the CATH (27) and SCOP (28) data-
bases. Due to copyright restrictions, links are provided to
retrieve data from the SCOP website rather than reprodu-
cing SCOP data on our pages.
The structure tab is designed to ease searching for all
orthologous clusters with a particular fold. This is accom-
plished by either direct or iterative searching for a particu-
lar CATH ID number. The direct searching method would be
to enter a known CATH ID into the PROFESSor to find the
correlated orthologous clusters. In iterative searching,
a user first searches for a protein structure with the
PROFESSor to identify the orthologous group, finds the
CATH ID in the structure tab, and then searches the selected
CATH ID with the PROFESSor. Both searching methods will
generate a list of orthologous clusters that contain the pro-
tein fold of interest.
The structure level also contains all pairwise structure
alignments of an orthologous cluster. The pairwise struc-
ture comparison tool DaliLite (29, 30) was used to measure
the backbone structure similarity of proteins within each
orthologous cluster defined by the eggNOG database.
All-against-all pairwise structural comparisons were carried
out for all 224 847 NOGs with 401 967 total structure com-
parisons. Structure calculations were completed with help
from the Holland Computing Center of the University of
Nebraska-Lincoln.
The Dali Z-scores were normalized to calculate a fraction-
al structure similarity (FSS) score: FSS = ZAB/ZAA, where ZAB is
the Dali Z-score when protein B is compared to protein A
and ZAA is the Z-score when protein A is compared to itself.
Thus, ZAA represents the maximum Z-score that can be
achieved for perfect similarity. FSS provides a simple nor-
malized and quantitative measure of the distance the two
proteins have diverged in their structures.
Sequence
The sequence tab of PROFESS lists all protein sequences
within the orthologous cluster. The sequence tab also pro-
vides the Uniprot accession numbers, molecular weight,
length of sequence and when available the structure.
A list of all sequences from each orthologous group is
downloadable into FASTA format and each sequence can
be individually copied and pasted into a text document in
FASTA format.
Diseases
The diseases tab of PROFESS is reserved for gene and pro-
tein information identified throughout the literature as
being involved in various human diseases. Currently,
PROFESS includes information about genes and proteins
involved in pancreatic cancer but this level of PROFESS
will grow rapidly as new data is incorporated.
Query system for data mining
The PROFESSor
The primary search function of PROFESS is the PROFESSor
(Figure 4), a unified text field that will assist the user to
easily refine complex queries by dynamically suggesting
entries from any integrated database. The PROFESSor as-
sists the user by correcting for spelling errors using
Levenshtein metrics, as well as providing a user defined
focused browsing feature. For instance, upon typing in
the query ‘collagenase’, the PROFESSor returns a drop
down list of protein folds and functions that have known
relation with collagenase (Figure 4). If a user selects the fold
(CATH) suggestion, PROFESS will return all functional clus-
ters known to contain that fold. The PROFESSor searches
all other data sources within PROFESS in the same manner.
.............................................................................................................................................................................................................................................................................................
Page 5 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
Page 6
In a single search, for example, the user can identify other
protein functions with the same fold, similar ligands, or
cellular localizations.
The PROFESSor may also be queried using many key-
words from several databases using boolean logic. Using
regular expressions, the general syntax for queries is
defined as:
KEY½ 0,1nw OR½ nwð Þ
OR½ 0,1 KEY½ 0,1nw OR½ nwð Þ
KEY depends on the database and may be one of the fol-
lowing (note that this list will grow with the number of
core databases): ALL, CATH, EC, GO, LIGAND, NOG, PDB,
PFAM, TAXON or UNIPORT. By default, all keywords after
a [KEY] are considered as a unique string for the query. The
superscripts 0, 1 and * mean not used, used only once and
used an arbitrary number of times, respectively. This behav-
ior can be altered by prefixing the keywords with [OR]. The
wildcard characters % (any number n of characters, with
n> 0) and _ (exactly one character) may be used in a
query. A logical AND is performed between different keys.
Advanced query system
Although the default views aims to provide a broad over-
view of protein functions, evolution, structures and se-
quences, users may need to create their own module—or
view—to mine only those pieces of data required to answer
a specific query. New views can be easily implemented
using SQL queries, which give users full access to any data
integrated within PROFESS. An example of an SQL query is
shown in Figure 5A and is discussed below in the
Applications section. The entity-relationship diagram
describing the structure of PROFESS is provided in the
online documentation and will help users to design the
SQL queries. Like other modules, the data displayed in
the custom view can be sorted and clustered as needed.
The data can also be downloaded in CSV format for further
analysis.
Functional-style query system
A fundamental component of PROFESS queries is to enable
the users to incorporate a variety of new functions, which
take as input a set of parameters and give as output a
well-defined value or set of values. Such user-defined func-
tions arise naturally in many applications. For example, we
defined the CPASS similarity function that is capable of
generating novel data relationships between proteins
based on a sequence and structure similarity in ligand-
binding sites (31). As another example, one may query for
a relationship between the PFAM and the eggNOG data-
bases, even though this relation is not explicitly defined in
the PROFESS database. The first atomic function to be inte-
grated is BLAST, which will be added shortly. It will enable
users to retrieve orthologous clusters of proteins related to
a protein sequence of interest. Input sequences will be
aligned against all sequences from the eggNOG database.
Figure 4. The PROFESSor query system. The PROFESSor is a dynamic search tool generated from the core databases to help the
user to refine complex queries. Using the PROFESSor users are given suggestions for extending their search words/phrases that
helps them rapidly and accurately find all functional, structure and sequence information about a particular protein and its
relation to other protein functions, folds or ligands.
.............................................................................................................................................................................................................................................................................................
Page 6 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
protein functions with the same fold, similar ligands, or
cellular localizations.
The PROFESSor may also be queried using many key-
words from several databases using boolean logic. Using
regular expressions, the general syntax for queries is
defined as:
KEY½ 0,1nw OR½ nwð Þ
OR½ 0,1 KEY½ 0,1nw OR½ nwð Þ
KEY depends on the database and may be one of the fol-
lowing (note that this list will grow with the number of
core databases): ALL, CATH, EC, GO, LIGAND, NOG, PDB,
PFAM, TAXON or UNIPORT. By default, all keywords after
a [KEY] are considered as a unique string for the query. The
superscripts 0, 1 and * mean not used, used only once and
used an arbitrary number of times, respectively. This behav-
ior can be altered by prefixing the keywords with [OR]. The
wildcard characters % (any number n of characters, with
n> 0) and _ (exactly one character) may be used in a
query. A logical AND is performed between different keys.
Advanced query system
Although the default views aims to provide a broad over-
view of protein functions, evolution, structures and se-
quences, users may need to create their own module—or
view—to mine only those pieces of data required to answer
a specific query. New views can be easily implemented
using SQL queries, which give users full access to any data
integrated within PROFESS. An example of an SQL query is
shown in Figure 5A and is discussed below in the
Applications section. The entity-relationship diagram
describing the structure of PROFESS is provided in the
online documentation and will help users to design the
SQL queries. Like other modules, the data displayed in
the custom view can be sorted and clustered as needed.
The data can also be downloaded in CSV format for further
analysis.
Functional-style query system
A fundamental component of PROFESS queries is to enable
the users to incorporate a variety of new functions, which
take as input a set of parameters and give as output a
well-defined value or set of values. Such user-defined func-
tions arise naturally in many applications. For example, we
defined the CPASS similarity function that is capable of
generating novel data relationships between proteins
based on a sequence and structure similarity in ligand-
binding sites (31). As another example, one may query for
a relationship between the PFAM and the eggNOG data-
bases, even though this relation is not explicitly defined in
the PROFESS database. The first atomic function to be inte-
grated is BLAST, which will be added shortly. It will enable
users to retrieve orthologous clusters of proteins related to
a protein sequence of interest. Input sequences will be
aligned against all sequences from the eggNOG database.
Figure 4. The PROFESSor query system. The PROFESSor is a dynamic search tool generated from the core databases to help the
user to refine complex queries. Using the PROFESSor users are given suggestions for extending their search words/phrases that
helps them rapidly and accurately find all functional, structure and sequence information about a particular protein and its
relation to other protein functions, folds or ligands.
.............................................................................................................................................................................................................................................................................................
Page 6 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 7
NOG clusters corresponding to significant hits will then be
returned to the user. By providing a library of standard
atomic functions, such as BLAST, the users will be able to
compose the atomic functions in complex functional-style
queries. A functional-style query is defined as a pipeline of
any of the atomic functions, where the output of a function
serves as input of the next function in the pipeline. The full
description of the current set of functions in PROFESS will
be available in the online documentation.
Method for data integration
Traditional data integration methods involve data ware-
housing, where the database extracts, transforms and
loads (ETL) data from various sources into a single schema
that is easy to query (Figure 1A). However ETL methods lack
flexibility because they require the warehouse schema to
be tightly coupled with the data sources. As a result, inte-
grating new data sources requires considerable effort as
the entire warehouse and subsequent queries need to be
redefined. The warehouse schema may also have to be re-
designed if one of the data sources schema changes after
an update.
LAV method
To address the flexibility issues of widely-used ETL methods,
the PROFESS database was designed using a flexible LAV
method (12, 32) as shown in Figure 1B. LAV methods in-
volve wrappers that provide an abstraction layer for each
data set. Wrappers are software that translate the data
sources and provide an abstract, simplified view of the inte-
grated data sources. Although there have been prior inte-
gration efforts of structural data and functional data
sources, the PROFESS system has a unique approach. It cre-
ates two internal wrappers, one for the integrated func-
tional data and another for the integrated structural
data. Then, it applies novel functions for the association
between these two wrappers. This multi-step integration
approach first merges the easier-to-integrate data sources,
and then merges the harder-to-integrate data sources.
Incomplete and incorrect information in the data source is
one of the major difficulties with data integration. By first
merging together closely related data sources, our method
increases the likelihood that data from different sources
will complete and correct each other. All of the annotations
are reported to the user who can then use them to assess
possible mis-annotations. In this way, PROFESS will help
users overcome such problems as incomplete and
Figure 5. Identification of potential pancreatic cancer drug targets. (A) An example SQL query used to parse PROFESS to gen-
erate protein–protein interaction networks between pancreatic cancer-related proteins. Select (green) only the information
relevant to solve the stipulated question from the dynamic join of relevant views from PROFESS (blue). The results are then
filtered to mine only interactions involving proteins of interest (red). Parts of the query related to the first interactor are shown
in darker colors, whereas sections of the query related to the second interactor are shown in lighter colors. (B) The SQL query on
PROFESS resulted in a list of protein-protein interactions among the set of pancreatic cancer-related proteins. The interaction
networks were displayed using Cytoscape (55). Identifying proteins that are part of a larger network provides one method to
prioritize potential therapeutic targets among the set of pancreatic cancer-related proteins.
.............................................................................................................................................................................................................................................................................................
Page 7 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
returned to the user. By providing a library of standard
atomic functions, such as BLAST, the users will be able to
compose the atomic functions in complex functional-style
queries. A functional-style query is defined as a pipeline of
any of the atomic functions, where the output of a function
serves as input of the next function in the pipeline. The full
description of the current set of functions in PROFESS will
be available in the online documentation.
Method for data integration
Traditional data integration methods involve data ware-
housing, where the database extracts, transforms and
loads (ETL) data from various sources into a single schema
that is easy to query (Figure 1A). However ETL methods lack
flexibility because they require the warehouse schema to
be tightly coupled with the data sources. As a result, inte-
grating new data sources requires considerable effort as
the entire warehouse and subsequent queries need to be
redefined. The warehouse schema may also have to be re-
designed if one of the data sources schema changes after
an update.
LAV method
To address the flexibility issues of widely-used ETL methods,
the PROFESS database was designed using a flexible LAV
method (12, 32) as shown in Figure 1B. LAV methods in-
volve wrappers that provide an abstraction layer for each
data set. Wrappers are software that translate the data
sources and provide an abstract, simplified view of the inte-
grated data sources. Although there have been prior inte-
gration efforts of structural data and functional data
sources, the PROFESS system has a unique approach. It cre-
ates two internal wrappers, one for the integrated func-
tional data and another for the integrated structural
data. Then, it applies novel functions for the association
between these two wrappers. This multi-step integration
approach first merges the easier-to-integrate data sources,
and then merges the harder-to-integrate data sources.
Incomplete and incorrect information in the data source is
one of the major difficulties with data integration. By first
merging together closely related data sources, our method
increases the likelihood that data from different sources
will complete and correct each other. All of the annotations
are reported to the user who can then use them to assess
possible mis-annotations. In this way, PROFESS will help
users overcome such problems as incomplete and
Figure 5. Identification of potential pancreatic cancer drug targets. (A) An example SQL query used to parse PROFESS to gen-
erate protein–protein interaction networks between pancreatic cancer-related proteins. Select (green) only the information
relevant to solve the stipulated question from the dynamic join of relevant views from PROFESS (blue). The results are then
filtered to mine only interactions involving proteins of interest (red). Parts of the query related to the first interactor are shown
in darker colors, whereas sections of the query related to the second interactor are shown in lighter colors. (B) The SQL query on
PROFESS resulted in a list of protein-protein interactions among the set of pancreatic cancer-related proteins. The interaction
networks were displayed using Cytoscape (55). Identifying proteins that are part of a larger network provides one method to
prioritize potential therapeutic targets among the set of pancreatic cancer-related proteins.
.............................................................................................................................................................................................................................................................................................
Page 7 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
Page 8
misleading data annotations. Structural and functional
data are often difficult to integrate because of different
identification numbers, different functional definitions,
and the absence of a direct link between the two data
sources. Our multi-level integration approach first links all
intermediate information to either the central functional
wrapper (as defined by the eggNOG database) or to the
central structural wrapper (as defined by the PDB data-
base). The PDB-eggNOG bridge then serves as the inter-
mediary for linking the functional and the structural
wrappers. If this linkage does not exist, then the protein
is not included in PROFESS.
The final step to achieve our flexibility and extendibility
goals was to normalize our database structure. Database
normalization was introduced by Codd in 1970 (33). It is a
systematic process to ensure that a database structure will
not be subject to anomalies after insertion, update, and
deletion, that could lead to a loss of data integrity (34).
Data normalization is also useful to reduce the need for
restructuring the collection of relations as new types of
data are introduced. There are currently five normal
forms. The higher the normal form, the more robust the
database structure is against inconsistencies. PROFESS was
designed using the fifth normal form proposed by Fagin
(35). The resulting entity-relationship diagram is shown in
Figure 1.
However, selective denormalization was subsequently
performed for performance reasons (36). In particular, the
PROFESSor queries data from the table precalc_professor
includes pre-computed joins between relations instead of
using a dynamic view. To maintain data consistency, rou-
tines were implemented along with the wrappers to regen-
erate this table whenever new data is inserted into
PROFESS.
Applications
Homologous protein structure comparison
PROFESS was initially created to test the hypothesis that
proteins experience uniform structural drift following the
divergence from a common ancestor. The goal of this effort
was to address an apparent paradox in structural biology.
Protein structures are generally considered invariant to
maintain function (37), but sequence determines structure
and sequence changes are the major determinant of evo-
lution (38, 39). Therefore, what is the impact to a structure
as a protein’s sequence undergoes genetic drift? Answering
this question is conceptually straight-forward and simply
required the structural comparison of functionally identical
proteins from different phyla. Since the PDB is richest in
bacterial proteins, functionally and evolutionarily similar
protein structures from the two most populated bacterial
phyla, Proteobacteria and Firmicutes, were the obvious
choice. Thus, a key component of this analysis was the iden-
tification and extraction of Proteobacteria and Firmicutes
protein structures from the PDB with an identical function-
al classification. Since the PDB is a classic example of a ware-
house database with limited query capabilities, it was not
possible to obtain this information directly from the PDB,
and was our impetus to develop PROFESS. PROFESS was
then used to associate PDB structures with both the
eggNOG (evolutionary genealogy of genes: non-supervised
orthologous groups) and phyla classifications. From this
dataset, we identified 281 unique NOGs that contained
a minimum of two Firmicutes organisms and two
Proteobacteria organisms with a total of 3047 bacterial pro-
teins (1066 Firmicutes and 1981 Proteobacteria). This set
was subjected to a pairwise structural comparison between
Proteobacteria–Proteobacteria structures, Firmicutes–
Firmicutes structures and Proteobacteria–Firmicutes struc-
tures. The result was a greater difference between the
Proteobacteria–Firmicutes structures, consistent with the
ancient split between the two phyla. The results were incor-
porated into the PROFESS database.
Identification of potential pancreatic cancer
therapeutic targets
Pancreatic cancer has the lowest five-year survival rate
(5.5%) among cancers and is the fourth leading cause of
cancer death in the USA (40, 41). Only three drugs have
been approved by the FDA to treat pancreatic cancer,
5-fluorouracil (42), gemcitabine (43) and erlotinib (44),
where these drugs are generally minimally effective and
do not significantly prolong life (45). Thus, real progress
in treating pancreatic cancer requires the identification of
truly novel, yet druggable protein targets (46). One ap-
proach is to advance existing genomics and proteomics stu-
dies that populate the literature. Capitalizing on these
existing data sets may provide a mechanism to identify po-
tential drug discovery targets. Five separate proteomic stu-
dies have classified a total of 802 unique proteins that were
differentially expressed in various pancreatic cancer cell
lines (47–51). Similarly, a recent genomics analysis of muta-
tion frequency rates in 24 pancreatic cancer cell lines iden-
tified 1331 genes with at least one genetic alteration (52).
To demonstrate the ease with which new data can be
integrated into PROFESS and the flexibility of PROFESS to
identify previously unknown relationships, PROFESS was
used to test the hypothesis that the proteomic and func-
tional genomics analysis of pancreatic cancer cells can be
used to identify potential drug discovery targets. Even
though changes in the expression profiles or a high muta-
tion rates are not sufficient to verify that the protein is
disease-related or therapeutically important (53, 54), it is
possible that the discovery of protein–protein interactions
networks could very well lead to possible drug targets
among the dataset of pancreatic cancer-related proteins.
.............................................................................................................................................................................................................................................................................................
Page 8 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
data are often difficult to integrate because of different
identification numbers, different functional definitions,
and the absence of a direct link between the two data
sources. Our multi-level integration approach first links all
intermediate information to either the central functional
wrapper (as defined by the eggNOG database) or to the
central structural wrapper (as defined by the PDB data-
base). The PDB-eggNOG bridge then serves as the inter-
mediary for linking the functional and the structural
wrappers. If this linkage does not exist, then the protein
is not included in PROFESS.
The final step to achieve our flexibility and extendibility
goals was to normalize our database structure. Database
normalization was introduced by Codd in 1970 (33). It is a
systematic process to ensure that a database structure will
not be subject to anomalies after insertion, update, and
deletion, that could lead to a loss of data integrity (34).
Data normalization is also useful to reduce the need for
restructuring the collection of relations as new types of
data are introduced. There are currently five normal
forms. The higher the normal form, the more robust the
database structure is against inconsistencies. PROFESS was
designed using the fifth normal form proposed by Fagin
(35). The resulting entity-relationship diagram is shown in
Figure 1.
However, selective denormalization was subsequently
performed for performance reasons (36). In particular, the
PROFESSor queries data from the table precalc_professor
includes pre-computed joins between relations instead of
using a dynamic view. To maintain data consistency, rou-
tines were implemented along with the wrappers to regen-
erate this table whenever new data is inserted into
PROFESS.
Applications
Homologous protein structure comparison
PROFESS was initially created to test the hypothesis that
proteins experience uniform structural drift following the
divergence from a common ancestor. The goal of this effort
was to address an apparent paradox in structural biology.
Protein structures are generally considered invariant to
maintain function (37), but sequence determines structure
and sequence changes are the major determinant of evo-
lution (38, 39). Therefore, what is the impact to a structure
as a protein’s sequence undergoes genetic drift? Answering
this question is conceptually straight-forward and simply
required the structural comparison of functionally identical
proteins from different phyla. Since the PDB is richest in
bacterial proteins, functionally and evolutionarily similar
protein structures from the two most populated bacterial
phyla, Proteobacteria and Firmicutes, were the obvious
choice. Thus, a key component of this analysis was the iden-
tification and extraction of Proteobacteria and Firmicutes
protein structures from the PDB with an identical function-
al classification. Since the PDB is a classic example of a ware-
house database with limited query capabilities, it was not
possible to obtain this information directly from the PDB,
and was our impetus to develop PROFESS. PROFESS was
then used to associate PDB structures with both the
eggNOG (evolutionary genealogy of genes: non-supervised
orthologous groups) and phyla classifications. From this
dataset, we identified 281 unique NOGs that contained
a minimum of two Firmicutes organisms and two
Proteobacteria organisms with a total of 3047 bacterial pro-
teins (1066 Firmicutes and 1981 Proteobacteria). This set
was subjected to a pairwise structural comparison between
Proteobacteria–Proteobacteria structures, Firmicutes–
Firmicutes structures and Proteobacteria–Firmicutes struc-
tures. The result was a greater difference between the
Proteobacteria–Firmicutes structures, consistent with the
ancient split between the two phyla. The results were incor-
porated into the PROFESS database.
Identification of potential pancreatic cancer
therapeutic targets
Pancreatic cancer has the lowest five-year survival rate
(5.5%) among cancers and is the fourth leading cause of
cancer death in the USA (40, 41). Only three drugs have
been approved by the FDA to treat pancreatic cancer,
5-fluorouracil (42), gemcitabine (43) and erlotinib (44),
where these drugs are generally minimally effective and
do not significantly prolong life (45). Thus, real progress
in treating pancreatic cancer requires the identification of
truly novel, yet druggable protein targets (46). One ap-
proach is to advance existing genomics and proteomics stu-
dies that populate the literature. Capitalizing on these
existing data sets may provide a mechanism to identify po-
tential drug discovery targets. Five separate proteomic stu-
dies have classified a total of 802 unique proteins that were
differentially expressed in various pancreatic cancer cell
lines (47–51). Similarly, a recent genomics analysis of muta-
tion frequency rates in 24 pancreatic cancer cell lines iden-
tified 1331 genes with at least one genetic alteration (52).
To demonstrate the ease with which new data can be
integrated into PROFESS and the flexibility of PROFESS to
identify previously unknown relationships, PROFESS was
used to test the hypothesis that the proteomic and func-
tional genomics analysis of pancreatic cancer cells can be
used to identify potential drug discovery targets. Even
though changes in the expression profiles or a high muta-
tion rates are not sufficient to verify that the protein is
disease-related or therapeutically important (53, 54), it is
possible that the discovery of protein–protein interactions
networks could very well lead to possible drug targets
among the dataset of pancreatic cancer-related proteins.
.............................................................................................................................................................................................................................................................................................
Page 8 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 9
The manually curated pancreatic cells ‘omics’ data
(PCOD) was integrated into PROFESS by implementing a
wrapper and creating a new relationship in the database.
The first issue addressed was that PCOD entries were iden-
tified by UniProt IDs, but the genes from the database of
interacting protein (DIP) are identified using GIs. Using a
standard ETL method would have required a program to
create a new table that contains data from PCOD, DIP and
the mapping between the UniProt IDs and GIs. Similar
tables would have to be created for any additional relation-
ship of interest to PCOD, which would led to an exponen-
tially growth in the number of tables. Instead, our PROFESS
database can take advantage of any data that has already
been integrated into the database. Specifically, the
UniProtKB mapping between UniProt IDs and GIs can be
used in SQL queries to create new dynamic views. In this
manner, PROFESS was mined to generate the view kog_in-
teracting_cancer_protein, functional clusters of interacting
pancreatic cancer-related proteins using the SQL statement
shown in Figure 5A. The protein interaction network was
quickly visualized (Figure 5B) by importing the output of
the PROFESS SQL query into Cytoscape (55). Once a view
has been created by a user, it will be automatically updated
whenever relevant tables storing data from DIP and PCOD
are updated. The resulting protein interaction networks il-
lustrate the rapid data analysis that can be achieved using a
fully integrated and flexible database based on protein
function and structure. Using our LAV-based approach,
the view for functional clusters of interacting pancreatic
cancer-related proteins was obtained in less than four
hours. Obtaining an equivalent table using the ETL
method would have required a significant amount of add-
itional effort.
Data access
PROFESS is freely accessible through the URL http://cse.unl
.edu/profess and through our web-site http://bionmr-c1
.unl.edu/. Data can be downloaded as parseable files in
comma separated values (CSV) format from the web-
interface or using RESTful HTTP requests that may be
batched in scripts. Sequences and phylogenetic trees can
be downloaded in FASTA and PHYLIP formats, respectively.
Implementation
The PROFESS database relies on the MySQL database man-
agement system. Wrappers are implemented in Java 1.6
and are platform independent. The web-user interface is
implemented in PHP, Dynamic HTML, and the general
Asynchronous Javascript and XML (AJAX) frameworks de-
veloped by Yahoo! (http://developer.yahoo.com/yui/) and
ExtJS (http://extjs.com). PROFESS is running under Open
SuSE Linux 11.0 on our new SunFire x4600 server, which
features 8 AMD quad-core processors (32 cores) and 64
GB of memory.
Future directions
The initial implementation of PROFESS has focused on data
integration and the development of basic searching cap-
abilities. The future development of PROFESS will focus
on the implementation of more robust user-friendly search-
ing capabilities to augment the PROFESSor and SQL queries.
Also, we will continue to expand PROFESS by the addition
of other databases that contain information relevant to the
structure, function and evolution of proteins and their as-
sociation to human diseases. The identification of function-
al relationships depends on this essential information,
where our new similarity and searching capabilities are ex-
pected to make associations not readily apparent within
the original datasets. Additionally, to create a robust tool
for functional annotation, the CPASS database (56) and re-
sults from functional screens of novel proteins by the
Functional Annotation Screening Technology by NMR
(FAST-NMR) (57, 58) will be integrated into PROFESS.
Finally, PROFESS provides a great opportunity as the
source data for many recent novel data mining and data
classification algorithms that are especially designed for
large-scale biological data (59).
Acknowledgement
The structure comparison work was completed utilizing the
Holland Computing Center of the University of Nebraska-
Lincoln. The content is solely the responsibility of the
authors and does not necessarily represent the official
views of the National Institute of Allergy and Infectious
Diseases.
Funding
This work was supported in part from the National Institute
of Allergy and Infectious Diseases (grant number
R21AI081154) to R.P. as well as by grants from the
Nebraska Tobacco Settlement Biomedical Research
Development Funds to R.P.; a Nebraska Research Council
Interdisciplinary Research Grant to R.P.; a Milton E. Mohr
Fellowship to T.T.; and a Fulbright Scholarship to P.R. The
research was performed in facilities renovated with support
from the National Institutes of Health (grant number
RR015468-01). Funding for open access charges: National
Institute of Allergy and Infectious Diseases (grant number
R21AI081154) to R.P.
Conflict of interest. None declared.
.............................................................................................................................................................................................................................................................................................
Page 9 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
(PCOD) was integrated into PROFESS by implementing a
wrapper and creating a new relationship in the database.
The first issue addressed was that PCOD entries were iden-
tified by UniProt IDs, but the genes from the database of
interacting protein (DIP) are identified using GIs. Using a
standard ETL method would have required a program to
create a new table that contains data from PCOD, DIP and
the mapping between the UniProt IDs and GIs. Similar
tables would have to be created for any additional relation-
ship of interest to PCOD, which would led to an exponen-
tially growth in the number of tables. Instead, our PROFESS
database can take advantage of any data that has already
been integrated into the database. Specifically, the
UniProtKB mapping between UniProt IDs and GIs can be
used in SQL queries to create new dynamic views. In this
manner, PROFESS was mined to generate the view kog_in-
teracting_cancer_protein, functional clusters of interacting
pancreatic cancer-related proteins using the SQL statement
shown in Figure 5A. The protein interaction network was
quickly visualized (Figure 5B) by importing the output of
the PROFESS SQL query into Cytoscape (55). Once a view
has been created by a user, it will be automatically updated
whenever relevant tables storing data from DIP and PCOD
are updated. The resulting protein interaction networks il-
lustrate the rapid data analysis that can be achieved using a
fully integrated and flexible database based on protein
function and structure. Using our LAV-based approach,
the view for functional clusters of interacting pancreatic
cancer-related proteins was obtained in less than four
hours. Obtaining an equivalent table using the ETL
method would have required a significant amount of add-
itional effort.
Data access
PROFESS is freely accessible through the URL http://cse.unl
.edu/profess and through our web-site http://bionmr-c1
.unl.edu/. Data can be downloaded as parseable files in
comma separated values (CSV) format from the web-
interface or using RESTful HTTP requests that may be
batched in scripts. Sequences and phylogenetic trees can
be downloaded in FASTA and PHYLIP formats, respectively.
Implementation
The PROFESS database relies on the MySQL database man-
agement system. Wrappers are implemented in Java 1.6
and are platform independent. The web-user interface is
implemented in PHP, Dynamic HTML, and the general
Asynchronous Javascript and XML (AJAX) frameworks de-
veloped by Yahoo! (http://developer.yahoo.com/yui/) and
ExtJS (http://extjs.com). PROFESS is running under Open
SuSE Linux 11.0 on our new SunFire x4600 server, which
features 8 AMD quad-core processors (32 cores) and 64
GB of memory.
Future directions
The initial implementation of PROFESS has focused on data
integration and the development of basic searching cap-
abilities. The future development of PROFESS will focus
on the implementation of more robust user-friendly search-
ing capabilities to augment the PROFESSor and SQL queries.
Also, we will continue to expand PROFESS by the addition
of other databases that contain information relevant to the
structure, function and evolution of proteins and their as-
sociation to human diseases. The identification of function-
al relationships depends on this essential information,
where our new similarity and searching capabilities are ex-
pected to make associations not readily apparent within
the original datasets. Additionally, to create a robust tool
for functional annotation, the CPASS database (56) and re-
sults from functional screens of novel proteins by the
Functional Annotation Screening Technology by NMR
(FAST-NMR) (57, 58) will be integrated into PROFESS.
Finally, PROFESS provides a great opportunity as the
source data for many recent novel data mining and data
classification algorithms that are especially designed for
large-scale biological data (59).
Acknowledgement
The structure comparison work was completed utilizing the
Holland Computing Center of the University of Nebraska-
Lincoln. The content is solely the responsibility of the
authors and does not necessarily represent the official
views of the National Institute of Allergy and Infectious
Diseases.
Funding
This work was supported in part from the National Institute
of Allergy and Infectious Diseases (grant number
R21AI081154) to R.P. as well as by grants from the
Nebraska Tobacco Settlement Biomedical Research
Development Funds to R.P.; a Nebraska Research Council
Interdisciplinary Research Grant to R.P.; a Milton E. Mohr
Fellowship to T.T.; and a Fulbright Scholarship to P.R. The
research was performed in facilities renovated with support
from the National Institutes of Health (grant number
RR015468-01). Funding for open access charges: National
Institute of Allergy and Infectious Diseases (grant number
R21AI081154) to R.P.
Conflict of interest. None declared.
.............................................................................................................................................................................................................................................................................................
Page 9 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
Page 10
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project in 2006. Nucleic Acids Res., 34, D322–D326.
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iterative algorithm for multiple structure alignment.
Bioinformatics, 21, 3255–3263.
26. Zhang,R. and Lin,Y. (2009) DEG 5.0, a database of essential genes
in both prokaryotes and eukaryotes. Nucleic Acids Res., 37,
D455–D458.
27. Cuff,A.L., Sillitoe,I., Lewis,T. et al. (2009) The CATH classification
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structural divergence in superfamilies. Nucleic Acids Res., 37,
D310–D314.
28. Andreeva,A., Howorth,D., Chandonia,J.M. et al. (2008) Data growth
and its impact on the SCOP database: new developments. Nucleic
Acids Res., 36, D419–D425.
29. Holm,L., Kaariainen,S., Rosenstrom,P. and Schenkel,A. (2008)
Searching protein structure databases with DaliLite v.3.
Bioinformatics, 24, 2780–2781.
30. Holm,L. and Park,J. (2000) DaliLite workbench for protein structure
comparison. Bioinformatics, 16, 566–567.
31. Powers,R., Copeland,J.C., Germer,K. et al. (2006) Comparison of pro-
tein active site structures for functional annotation of proteins and
drug design. PROTEINS: Struct. Funct. Bioinformatics, 65, 124–135.
32. Rachel,P. and Alon,H. (2001) MiniCon: a scalable algorithm for
answering queries using views. The VLDB J., 10, 182–198.
33. Codd,E.F. (1970) A relational model for large shared data banks.
Commun. ACM, 13, 377–387.
34. Codd,E.F. (1990) The Relational Model for Database Management:
Version 2. Addison-Wesley Longman Publishing Co. Inc., Boston,
MA, USA.
35. Fagin,R. (1981) A normal form for relational databases that is
based on domains and keys. ACM Trans. Database Systems, 6,
387–415.
36. Date,C.J. (2005) Database in Depth: Relational Theory for
Practitioners. O’Reilly Media Inc., Sebastopol, CA USA.
37. Forouhar,F., Kuzin,A., Seetharaman,J. et al. (2007) Functional
insights from structural genomics. J. Struct. Funct. Genomics, 8,
37–44.
38. Chothia,C. and Lesk,A.M. (1986) The relation between the diver-
gence of sequence and structure in proteins. Embo J., 5, 823–826.
39. Rost,B. (1999) Twilight zone of protein sequence alignments.
Protein Eng., 12, 85–94.
40. Sarkar,F.H., Banerjee,S. and Li,Y. (2007) Pancreatic cancer:
Pathogenesis, prevention and treatment. Toxicol. Appl.
Pharmacol., 224, 326–336.
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Cancer J. Clin., 59, 225–249.
42. Rich,T.A., Shepard,R.C. and Mosley,S.T. (2004) Four decades of con-
tinuing innovation with fluorouracil: current and future
approaches to fluorouracil chemoradiation therapy. J. Clin.
Oncol., 22, 2214–2232.
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use in the management of pancreatic cancer. Am. J. Cancer, 4,
395–416.
.............................................................................................................................................................................................................................................................................................
Page 10 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
1. Babu,P.A., Udyama,J., Kumar,R.K. et al. (2007) DoD2007: 1082
molecular biology databases. Bioinformation, 2, 64–67.
2. Galperin,M.Y. and Cochrane,G.R. (2009) Nucleic Acids Research
annual database issue and the NAR online molecular biology data-
base collection in 2009. Nucleic Acids Res., 37, D1–D4.
3. Navarro,D.J., Niranjan,V., Peri,S. et al. (2003) From biological data-
bases to platforms for biomedical discovery. Trends Biotechnol., 21,
263–268.
4. Stein,L. (2002) Creating a bioinformatics nation. Nature, 417,
119–120.
5. Horn,F., Vriend,G. and Cohen,F.E. (2001) Collecting and harvesting
biological data: the GPCRDB and NucleaRDB information systems.
Nucleic Acids Res., 29, 346–349.
6. Stevens,R., Goble,C., Baker,P. and Brass,A. (2001) A classification of
tasks in bioinformatics. Bioinformatics, 17, 180–188.
7. Wong,L. (2002) Technologies for integrating biological data. Brief
Bioinform., 3, 389–404.
8. Davidson,S.B., Overton,C. and Buneman,P. (1995) Challenges in
integrating biological data sources. J. Comp. Biol., 2, 557–572.
9. Joyce,A.R. and Palsson,B.O. (2006) The model organism as a system:
integrating ’omics’ data sets. Nature Rev. Mol. Cell Biol., 7,
198–210.
10. Chen,Y. and Revesz,P. (2003) Proceedings of the 2003 IEEE/WIC
International Conference on Web Intelligence. IEEE Computer
Society; Washington, DC; Halifax, Canada, pp. 301–309.
11. Revesz,P. and Triplet,T. (2008) Reclassification of linearly classified
data using constraint databases. In: Proceedings of the Twelfth
East-European Conference on Advances of Databases and
Information Systems. Springer LNCS 5207; Pori, Finland, pp.
231–245.
12. Halevy,A.Y. (2001) Answering queries using views: a survey. VLDB
J.: Very Large Data Bases, 10, 270–294.
13. Berman,H.M., Westbrook,J., Feng,Z. et al. (2000) The protein data
bank. Nucleic Acids Res., 28, 235–242.
14. Jensen,L.J., Julien,P., Kuhn,M. et al. (2008) eggNOG: automated
construction and annotation of orthologous groups of genes.
Nucleic Acids Res., 36, D250–D254.
15. Muller,J., Szklarczyk,D., Julien,P. et al. (2010) eggNOG v2.0: extend-
ing the evolutionary genealogy of genes with enhanced non-super-
vised orthologous groups, species and functional annotations.
Nucleic Acids Res., 38, D190–D195.
16. The UniProt Consortium (2009) The Universal Protein Resource
(UniProt) 2009. Nucleic Acids Res., 37, D169–D174.
17. Finn,R.D., Tate,J., Mistry,J. et al. (2008) The Pfam protein families
database. Nucleic Acids Res., 36, D281–D288.
18. The Gene Ontology Consortium. (2006). The Gene Ontology (GO)
project in 2006. Nucleic Acids Res., 34, D322–D326.
19. Webb,E.C. (1992) Enzyme Nomenclature 1992: Recommendations
of the Nomenclature Committee of the International Union of
Biochemistry and Molecular Biology on the No (Enzyme
Nomenclature). Academic Press, San Diego, CA.
20. Kanehisa,M., Araki,M., Goto,S. et al. (2008) KEGG for linking gen-
omes to life and the environment. Nucleic Acids Res., 36,
D480–D484.
21. Arifuzzaman,M., Maeda,M., Itoh,A. et al. (2006) Large-scale identi-
fication of protein-protein interaction of Escherichia coli K-12.
Genome Res., 16, 686–691.
22. Salwinski,L., Miller,C.S., Smith,A.J. et al. (2004) The Database of
Interacting Proteins: 2004 update. Nucleic Acids Res., 32,
D449–D451.
23. Retief,J.D. (2000) Phylogenetic analysis using PHYLIP. Methods Mol.
Biol., 132, 243–258.
24. Felsenstein,J. (1989) PHYLIP – Phylogeny inference package (version
3.2). Cladistics, 5, 164–166.
25. Lupyan,D., Leo-Macias,A. and Ortiz,A.R. (2005) A new progressive-
iterative algorithm for multiple structure alignment.
Bioinformatics, 21, 3255–3263.
26. Zhang,R. and Lin,Y. (2009) DEG 5.0, a database of essential genes
in both prokaryotes and eukaryotes. Nucleic Acids Res., 37,
D455–D458.
27. Cuff,A.L., Sillitoe,I., Lewis,T. et al. (2009) The CATH classification
revisited–architectures reviewed and new ways to characterize
structural divergence in superfamilies. Nucleic Acids Res., 37,
D310–D314.
28. Andreeva,A., Howorth,D., Chandonia,J.M. et al. (2008) Data growth
and its impact on the SCOP database: new developments. Nucleic
Acids Res., 36, D419–D425.
29. Holm,L., Kaariainen,S., Rosenstrom,P. and Schenkel,A. (2008)
Searching protein structure databases with DaliLite v.3.
Bioinformatics, 24, 2780–2781.
30. Holm,L. and Park,J. (2000) DaliLite workbench for protein structure
comparison. Bioinformatics, 16, 566–567.
31. Powers,R., Copeland,J.C., Germer,K. et al. (2006) Comparison of pro-
tein active site structures for functional annotation of proteins and
drug design. PROTEINS: Struct. Funct. Bioinformatics, 65, 124–135.
32. Rachel,P. and Alon,H. (2001) MiniCon: a scalable algorithm for
answering queries using views. The VLDB J., 10, 182–198.
33. Codd,E.F. (1970) A relational model for large shared data banks.
Commun. ACM, 13, 377–387.
34. Codd,E.F. (1990) The Relational Model for Database Management:
Version 2. Addison-Wesley Longman Publishing Co. Inc., Boston,
MA, USA.
35. Fagin,R. (1981) A normal form for relational databases that is
based on domains and keys. ACM Trans. Database Systems, 6,
387–415.
36. Date,C.J. (2005) Database in Depth: Relational Theory for
Practitioners. O’Reilly Media Inc., Sebastopol, CA USA.
37. Forouhar,F., Kuzin,A., Seetharaman,J. et al. (2007) Functional
insights from structural genomics. J. Struct. Funct. Genomics, 8,
37–44.
38. Chothia,C. and Lesk,A.M. (1986) The relation between the diver-
gence of sequence and structure in proteins. Embo J., 5, 823–826.
39. Rost,B. (1999) Twilight zone of protein sequence alignments.
Protein Eng., 12, 85–94.
40. Sarkar,F.H., Banerjee,S. and Li,Y. (2007) Pancreatic cancer:
Pathogenesis, prevention and treatment. Toxicol. Appl.
Pharmacol., 224, 326–336.
41. Jemal,A., Siegel,R., Ward,E. et al. (2009) Cancer statistics, 2009. CA
Cancer J. Clin., 59, 225–249.
42. Rich,T.A., Shepard,R.C. and Mosley,S.T. (2004) Four decades of con-
tinuing innovation with fluorouracil: current and future
approaches to fluorouracil chemoradiation therapy. J. Clin.
Oncol., 22, 2214–2232.
43. Frampton,J.E. and Wagstaff,A.J. (2005) Gemcitabine: a review of its
use in the management of pancreatic cancer. Am. J. Cancer, 4,
395–416.
.............................................................................................................................................................................................................................................................................................
Page 10 of 11
Original article Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011
.............................................................................................................................................................................................................................................................................................
Page 11
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.............................................................................................................................................................................................................................................................................................
Page 11 of 11
Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
.............................................................................................................................................................................................................................................................................................
for advanced pancreatic cancer: targeting the epidermal growth
factor and vascular endothelial growth factor pathways.
Oncologist, 13, 289–298.
45. Morgan,G., Ward,R. and Barton,M. (2004) The contribution of cyto-
toxic chemotherapy to 5-year survival in adult malignancies. Clin.
Oncol., 16, 549–560.
46. Owens,J. (2007) Determining druggability. Nat. Rev. Drug
Discovery, 6, 187.
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Database, Vol. 2010, Article ID baq011, doi:10.1093/database/baq011 Original article
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