OralCard: A bioinformatic tool for the study of oral proteome

AOB-2940; No. of Pages 11

OralCard: A bioinformatic tool for the study of oral proteome

Joel P. Arrais a,*, Nuno Rosa b, Jose Melo a, Edgar D. Coelho a, Diana Amaral b,Maria Jose Correia b, Marlene Barros b,c, Jose Luıs Oliveira a

aDepartment of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA),

University of Aveiro, PortugalbDepartment of Health Sciences, Institute of Health Sciences, The Catholic University of Portugal, Viseu, PortugalcCentre for Neurosciences and Cell Biology, University of Coimbra, Portugal

a r c h i v e s o f o r a l b i o l o g y x x x ( 2 0 1 3 ) x x x – x x x

a r t i c l e i n f o

Article history:

Accepted 30 December 2012

Keywords:

Oral proteome

Microbiome

Diabetes melitus

OralCard

a b s t r a c t

Objectives: The molecular complexity of the human oral cavity can only be clarified through

identification of components that participate within it. However current proteomic tech-

niques produce high volumes of information that are dispersed over several online data-

bases. Collecting all of this data and using an integrative approach capable of identifying

unknown associations is still an unsolved problem. This is the main motivation for this

work.

Results: We present the online bioinformatic tool OralCard, which comprises results from 55

manually curated articles reflecting the oral molecular ecosystem (OralPhysiOme). It com-

prises experimental information available from the oral proteome both of human (OralOme)

and microbial origin (MicroOralOme) structured in protein, disease and organism.

Conclusions: This tool is a key resource for researchers to understand the molecular founda-

tions implicated in biology and disease mechanisms of the oral cavity. The usefulness of this

tool is illustrated with the analysis of the oral proteome associated with diabetes melitus

type 2. OralCard is available at http://bioinformatics.ua.pt/oralcard.

# 2013 Elsevier Ltd. All rights reserved.

Available online at www.sciencedirect.com

journal homepage: http://www.elsevier.com/locate/aob

1. Introduction

The human oral cavity is a complex ecosystem where human,

microbial and environmental factors interact in a dynamic

equilibrium. Understanding the biology of the oral cavity and

disorders that affect it (or systemic diseases that are reflected

in it) depends on the compilation and integration of informa-

tion generated by high-throughput techniques such as

proteomic studies, complemented with targeted studies based

on antibodies techniques. This is of particular relevance in the

oral cavity since it comprises proteins of endogenous (human)

and exogenous (microbiome) origin. Comprehending how

these different sets of proteins relate is of the utmost

* Corresponding author at: Instituto de Engenharia Electronica e TelemaPortugal. Tel.: +351 234 370 500; fax: +351 234 370 545.

E-mail address: [email protected] (J.P. Arrais).

Please cite this article in press as: Arrais JP, et al. OralCard: A bioinformaticdx.doi.org/10.1016/j.archoralbio.2012.12.012

0003–9969/$ – see front matter # 2013 Elsevier Ltd. All rights reservehttp://dx.doi.org/10.1016/j.archoralbio.2012.12.012

importance in understanding oral biology and also the

pathogenesis of oral diseases.1

Several sub-compartments contribute to the oral protein

composition, namely secretions from the major salivary

glands: the parotid, submandibular (SM) and sublingual (SL)

glands, making up 90% of the total salivary secretion. The

remaining 10% are contributions of a collection of the minor

salivary glands, the gingival crevicular fluid (GCF), the tongue

and the oral mucosa. Plasma proteins can reach the oral cavity

by several means, the most common being passive diffusion,

ultrafiltration, and as a result of GCF outflow contributing to

the oral protein composition.2 The pool of oral protein is also

enriched by molecules originating from the microbiome which

colonizes the oral surfaces. These microbial metabolites are

tica de Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro,

tool for the study of oral proteome. Archives of Oral Biology (2013), http://

d.

http://dx.doi.org/10.1016/j.archoralbio.2012.12.012

http://bioinformatics.ua.pt/oralcard

mailto:[email protected]



http://www.sciencedirect.com/science/journal/00039969


a r c h i v e s o f o r a l b i o l o g y x x x ( 2 0 1 3 ) x x x – x x x2


present in the oral cavity as secreted and may act on the

proteins present in the salivary secretions, altering them.3

Considering saliva as the fluid that reflects the protein

composition resulting from the contribution of the above-

mentioned oral sub-compartments and that it is readily

accessible in a non-invasive way, it has long been identified

as a diagnostic sample fluid for a swiftly growing range of

disease and clinical scenarios, as well as a candidate for

biomarker identification.4,5 Over the years there have been

many reviews of the genomics and proteomics of saliva. It has

been highlighted that saliva could be used in oral cancer

diagnosis,6 in the diagnosis of systemic diseases,7 in micro-

biome analysis,8 in psychobiological medicine,9 and in

forensic dentistry.10 The proteomic analysis of saliva, which

was mostly conducted by 2D electrophoresis/mass spectrom-

etry or 2D liquid chromatography/mass spectrometry,11

showed that the salivary proteome consisted of approximately

1.000 distinct protein sequences, by the year 2007.12 By the end

of 2010 this number had more than doubled, with identifica-

tion of 2290 salivary proteins.13 With few exceptions, the

microbial contribution to the oral proteome, although inferred

from the presence of a well-characterized oral microbiome

(e.g., HOMDd and HMPe) has not been the subject of saliva or

oral fluids proteomic analysis.

Biology databases can be focused on particular organisms,

such as Saccharomyces Genome Database (SGD)14 for Saccha-

romyces, or integrative databases, for instance, the Universal

Protein Resource (UniProt).15 Although UniProt is used

worldwide by the scientific community, its data is very broad,

which means researchers on very specific topics will only be

interested in a minuscule portion of each item in the database.

In addition, online data on specific topics is very sparse,

making the researcher’s task burdensome and extremely

time-consuming. These aspects motivate the development of

databases to be used by specific scientific communities

sharing the same research interests.

In previous work we have compiled the OralOme,16 a

collection of specific protein-related biomolecular data col-

lected from UniProt, Protein Data Bank (PDB),17 HUGO Gene

Nomenclature Committee (HGNC),18 Entrez Gene,19

Ensembl,20 Pharmacogenomics Knowledge Database

(PharmGKB),21 BRaunschweig ENzyme Database (BRENDA),22

Online Mendelian Inheritance in Man (OMIM),23 Kyoto

Encyclopaedia of Genes and Genomes (KEGG),24 and Gene

Ontology (GO).25 OralOme comprises experimental data

relative to 3397 non-redundant human oral proteins in healthy

and diseased states (605 altered in pathological conditions and

51 present only in disease), for instance, GO terms, homolo-

gies, pathways involved, and protein structure information.

In this paper we present OralCard, a Web Application that

allows mining over an integrative database containing

manually curated information about the oral cavity proteome

with the addition of the experimentally determined oral

proteome of microbial origin. OralCard promptly allows a wide

range of data associations, for instance, whether a protein is

involved in any specific pathological condition, which micro-

bial proteins may be present in the oral cavity, or what the

d www.homd.org/.e http://commonfund.nih.gov/hmp/.


annotated functions of any given protein are and in which

pathways it is implicated. Furthermore, for each protein it is

possible to explore the structural and functional details.

OralCard facilitates the interpretation of proteomic data of

the oral cavity and will therefore be a valuable resource for

researchers aiming to understand the physiologies of oral

cavity in health and disease and probing for potential

biomarkers for oral and systemic diseases.

2. Materials and methods

The development of the OralCard database was a combination

of manual and automatic processes. A thorough manual

bibliography review was performed to gather current knowl-

edge about all proteins identified in proteomic studies. The

protein information obtained from data curation was auto-

matically acquired from public biomedical databases and

integrated in the OralCard database. The OralCard was

conceived to allow the end-user to perform queries in a fast

and intuitive way.

The database and user interface were developed using

technologies that are currently state-of-the-art in information

systems, such as linked data, web services, service composi-

tion and interactions network visualization. These tools

helped us build and tune a set of automated processes to

capture information from various biological resources, sim-

plifying the construction of OralCard data warehouse.

Next, we present a general view of the entire process, from

the gathering of biological information to the development of

OralCard presentation layer (Fig. 1).

2.1. Manual data assembly and curation

The assembly of the Human Oralome, i.e. its compilation and

annotation, was performed mostly as described in16 with

several improvements and updates. The following PubMed

query was made: (‘‘proteomics’’ OR ‘‘proteomic’’ OR ‘‘prote-

ome’’) AND (‘‘saliva’’ [Title/Abstract] OR ‘‘oral’’ [Title/Ab-

stract]). The list of articles retrieved was manually inspected

and the identifiers from the proteins found in the various

references were collected. These studies12,26–79 (supplemental

Table 1) collected samples from different sources, i.e. parotid

glands, SM, SL, minor salivary glands, GCF, tongue mucosa and

oral mucosa. From the first publication of oral proteomes,

many of the original identified proteins, catalogued as

different entries in biological databases, have been merged

with others and some were deleted due to misidentification.

Therefore, all information concerning the identified proteins

was manually curated and updated. The update of the IPI

(International Protein Index) entries was carried out with ‘‘IPI

History search’’ online tool.80 All other updates have been

made according to UniProt database.81 Another addition to

OralOme was the inclusion of manually curated data on the

proteins, samples and techniques used. More specifically, for

each protein, the following data were added to the database

when available: the up/down regulation and fold change

regarding normal samples; post translation modifications; and

whether the protein had previously been proposed as a

biomarker. Regarding the sample collection, data such as


http://commonfund.nih.gov/hmp/

http://commonfund.nih.gov/hmp/



Edgar Duarte Coelho

Realce

Edgar Duarte Coelho

Realce

Fig. 1 – Workflow for manual curation of proteins and automatic annotation.

a r c h i v e s o f o r a l b i o l o g y x x x ( 2 0 1 3 ) x x x – x x x 3


stimulation or non-stimulation of saliva and donor data

(health/disease, age, sex and social habits) were also stored. In

studies where there were donors with disease, manual

annotation of the corresponding MeSH term and OMIM code

was also performed. The OMIM code was used to query web

services to retrieve specific information, such as disease name

and description. The usage of MeSH terms provides a widely

used vocabulary for disease annotation including conditions

such as caries or gingivitis which are non Mendelian diseases

and therefore not covered by the OMIM codes. Finally, all of

these data are mapped to the PubMed citation where they

were published.

Supplementary data associated with this article can be

found, in the online version, at http://dx.doi.org/10.1016/

j.archoralbio.2012.12.012.

A further improvement to the previous OralOme database

was the inclusion of microbial proteins experimentally

determined in proteomic studies of oral samples.77–79 Jagtap

et al.78 have made the first deep proteomic analysis of saliva

samples specifically targeting proteins of microbial origin.

This study presents some methodological approaches which

increased microbial protein identification by over one order of

magnitude. However, almost half of the microbial proteins

identified by Jagtap et al.78 were automatically annotated from

the genomic sequences of the Human Oral MicroBiome

Database and were not matched to an UniProt identifier. We

did not include these proteins in OralOme since there is still


little information and evidence of the actual existence of these

proteins.

Many of the proteins originally identified in the first

proteomic studies were misclassified as different entries in

the literature and in the biological databases. Some of these

incorrectly allocated proteins were either merged with others

or deleted. Many of the original entries in protein studies had

an International Protein Index (IPI)80 identifier, which ceased

functions in September of 2011. Therefore, the ‘‘IPI History

Search’’ tool was used to update the obsolete IPI entries. From

now on, the database information will be updated after the

UniProt database.

2.2. Automatic data annotation

A key objective of the OralCard project was to collect

information on proteins present in the oral cavity and

integrate that information, providing associations between

proteins, diseases, pathways, gene ontologies and organisms.

To achieve this goal, we carried out a first survey of online

sources where this information would be available.

We begin the information retrieval workflow with a

protein. For this particular case, the data source used was

UniProt, the most complete resource for protein sequence and

functional information. UniProt identifies each protein with

an accession code, which we will refer to as the UniProtKBAC

(UniProt Knowledgebase Accession Code). The UniProt service






Table 1 – Summary of the human and bacterial protein identified in the oral cavity.

Source Human OralOme Bacterial OralOme

#Entries #Proteins %Proteins #Entries #Proteins % Proteins

Organisms 1 3.523 100.0% 270 1.207 100.0%

UniProt 3.523 – – 1207 – –

MeSH 11 1.171 33.2% 3 3 0.2%

KEGG pathway 236 231 6.6% 20 18 1.5%

Gene ontology 4.621 3.346 95.0% 538 652 54.0%

PDB 7.675 1.154 33.0% 122 18 1.49%

PubMed 52 3.523 100.0% 5 1207 100.0%



provides several methods, which combined with program-

ming tools facilitate the retrieval of essential information

related to the protein, e.g., structural information, gene

nomenclature data, enzyme functional data, and even the

impact of genetic variation on drug response, obtained from

PDB, HGNC, BRENDA and PharmGKB, respectively.

Along with the UniProtKBAC, information manually anno-

tated, as described in the previous section, was also stored. The

procedures described above were used to build the OralOme

database. The OralCard interface was developed to automati-

cally retrieve and update the Oralome database with the

collected information and present it in a user-friendly way. This

is extremely useful as it joins in one single endpoint relevant

proteomic data concerning oral proteins, which increases the

time–efficiency ratio when searching for a protein, disease or

microorganism and allows for systematic approaches in

exploration of the oral proteome, as will be shown in the case

study. Table 1 summarizes up all extracted relations.

2.3. Implementation

The OralCard web interface was developed using Stripes, a

web framework that facilitates the development of Java web

applications. Stripes enabled full control over URLs, easing the

task of accessing an entity by only knowing its id. For instance,

there is direct access to the protein P22894 (neutrophil

collagenase) by introducing the address http://bioinformatic-

s.ua.pt/oralcard/proteins/view/P22894. OralCard is supported

by a MySQL Server Edition 5.1, and access to the data is

encapsulated by Hibernate, an object-relational mapping tool

for Java. All the services are running under the campus core

datacentre assuring fault tolerance, reliability and stability.

3. Results

This section includes detailed information about the main

features of OralCard, and how they are available to the end-

user. OralCard is organized in three distinct views, each with a

specific searching method: (1) by protein names or respective

UniProt codes, (2) by disease name, OMIM code or MeSH term,

(3) and by organism.

3.1. Protein view

When searching for a protein name or its UniProt code,

OralCard retrieves general information about the given

protein (Fig. 2a). The button in the upper right corner of the


page shows some details regarding protein name, gene name

and synonyms, and also UniProt, HGNC, Ensembl, Enzyme

Commission, and Entrez Gene accession codes.

When the information is available, it provides more specific

data, found in the menu on the left: gene information (Panther),

protein domain information (SMART), pharmacogenomic in-

formation (PharmaGKB), enzyme information (BRENDA), pro-

tein–protein interaction information (STRING), and structural

information (PDB). OralCard also lists the diseases and

bibliographic references associated with the given protein

and its biological sources, either in healthy or diseased states.

3.2. Disease view

The same procedure can be used to query OralCard by disease

name, OMIM code or Mesh term. When the query is done, the

user obtains the OMIM code for the disease and the option for

listing the proteins OralCard associates with the disease. This

protein list can be further explored with the tools on the left

panel, which includes organism, proteins, pathways, GOs and

mesh. With ‘‘Organism’’ the user obtains a list of the organism

producing the proteins in the initial list. The link ‘‘Proteins’’

lists all proteins that were identified in the manual curation of

the selected literature as being associated in OralOme both

from human or microbial origin.

Following this link the user can view which proteins are

reported to be deregulated during the course of the given

disease in proteomic studies, which GO terms are related to

these proteins, known biomarkers and the annotated post-

translation modifications (Fig. 2b).

From the initial disease view the user can also use the link

‘‘Pathways’’ where it is possible to view the pathways in which

these proteins participate and the percentage of proteins

involved.

Finally there are two more links available for the user: one

to the GO annotation of the proteins in the list and the other to

the MESH description of the disease.

3.3. Organism view

Using an organism as search term returns its taxonomy

information. OralCard also provides a list of the proteins

reported to be present in the oral cavity that are attributed to

that organism and the diseases in which these proteins are

involved (Fig. 2c).

These features demonstrate how the information is

integrated in OralCard: the user can perform a query in any

of the three views and navigate all data.


http://bioinformatics.ua.pt/oralcard/proteins/view/P22894

http://bioinformatics.ua.pt/oralcard/proteins/view/P22894



Fig. 2 – Interface of the main views provided by OralCard: (a) protein view; (b) disease view; (c) organism view.



4. Discussion

4.1. Diabetes mellitus type 2 (DMT2) case study

OralCard web tool is to be used in studies related to oral health,

but can also be used in systemic disorders presenting altered

proteins which can be detected in saliva. We chose diabetes

mellitus type 2 (DMT2) as a case study to demonstrate how the

information available on OralCard can be queried, understood

and related to the molecular mechanisms of this particular

disease. DMT2 is an example of a systemic disease of

multifactorial origins, in which different signalling and


metabolic pathways are compromised and which also has

implications for oral health, especially the risk of periodonti-

tis.82

One of the most common problems in diabetic patients is

impaired healing, closely related to blood coagulation, which

has a huge impact on oral medical care.83,84 Under normal

conditions, the coagulation system is characterized by a

constant balance between the processes of coagulation and

fibrinolysis. Since OralCard stores all the information on

proteins found in the oral cavity we can verify if there is

molecular evidence of the fibrinolysis/coagulation imbalance

in the oral proteome of these patients. This survey began with

identification of the proteins present in oral samples from type




Fig. 3 – OralCard disease view showing the list of pathways related to the proteins identified in diabetes mellitus, type 2. The

list of proteins involved in the complement and coagulation cascades is also showed.



II diabetic patients, using the disease search feature of the

OralCard (search for diabetes and choose ‘‘Diabetes Mellitus,

Type 2’’). This approach led to identification of 445 proteins

(Fig. 2b).

For type II diabetic patients, there are quantitative data,

and therefore it is possible to know which proteins have their

expression level altered (down or up regulated) and even find

the expression level fold change (Fig. 2b, ‘‘Regulation’’

column). This type of information allows the identification

of proteins whose expression is more altered in the disease.

Apart from information directly related to the oral proteins,

OralCard also stores information related to the samples such

as donor (age, gender and social habits), sampling and analysis

methods used.

After identifying the proteins present in saliva that are

altered in DMT2 patients (Fig. 1), we check which pathways

Fig. 4 – OralCard disease view showing a squematic representa


these proteins are involved in, allowing verification of the

molecular mechanisms compromised in this disease. To

accomplish that task, in the disease view, we must select

‘‘Pathways’’ from the Menu options (Fig. 3).

From the list of pathways identified as being altered in

DMT2, we can verify that the pathways with the highest

number of modified proteins in DMT2 are those corresponding

to metabolism42 and coagulation/complement cascades

(Fig. 3, #Proteins column).

After identification of proteins in DMT2 already recognized

as altered when compared to healthy donors and that these

proteins are involved in the blood coagulation cascade and

complement response, it is possible, in a next step, to know

exactly which proteins are altered so that their specific role

in the pathways can be identified. Twenty-two proteins

related to coagulation/complement pathway (Fig. 3, column

tion of the complement and coagulation cascades pathway.




Fig. 5 – OralCard view showing the molecular network around alpha-2-macroglobulin (minimum score = 900; results per

level = 10; depth = 2).



‘‘Proteins’’, selecting ‘‘show’’) are altered in the oral cavity of

DMT2 patients.

OralCard can be used to obtain information about the

changes in the expression of these proteins in DMT2 (Fig. 2b,

‘‘Regulation’’ column). It is evident that three of them alpha 1-

antitrypsin (A1AT) (fold change of 3.2�), alpha-2 macroglobu-

lin (A2MG) (2.2�) and complement component C6 (4.8�) are

highly increased.

The disease view can be used to show what are the

molecular functions of the altered proteins. For instance, in

this example we see that alpha 1-antitrypsin and alpha-2

macroglobulin both have ‘‘serine-type endopeptidase inhibi-

tor activity’’ (Fig. 2b, expanding protein information ‘‘+’’).

Considering that blood coagulation is a series of serine

protease cascade reactions, A1AT and A2MG are key points of

regulation in this pathway (Fig. 4).

Fig. 6 – OralCard protein view showing structural information (

(A1AT).


The central role of these two proteins in the coagulation

cascade is obvious from analysis of the molecular interaction

networks of A2MG and A1AT, for they interact with several key

molecules like kallikrein (KLKB1), plasminogen (PLMN) or

plasminogen activator inhibitor 1 (PAI1) (Fig. 5), that is,

proteins involved in fibrinolysis, a mechanism closely related

to the regulation of blood coagulation.

At this point it might seem odd that using OralCard it was

possible to identify an increase in the serine protease

inhibitors A1AT and A2MG involved in the regulation of

fibrinolysis/coagulation, for it is known that DMT2 patients are

characterized by hypercoagulable and hypofibrinolysis

states.85 These facts suggest that these two inhibitors,

although in higher quantities in DMT2 patients, may not be

functional. Hyperglycaemia in DMT2 causes non-enzymatic

glycosylation of proteins, leading to various complications like

PDB tab in left menu) for the protein alpha-1-antitrypsin






nephropathy, retinopathy, neuropathy and angiopathy.86

Thus, it was important to verify if the inhibitors A1AT and

A2MG have sequences liable to non-enzymatic glycosylation

that may affect their function.

Since OralCard allows exploration of structural informa-

tion relative to oral proteins, it is possible to determine the

existence of non-enzymatic glycosylation sites capable of

influencing the function of A1AT and A2MG. The structure of

the protein complex composed of A1AT and one of the serine

proteases which it inhibits (trypsin) was obtained from

OralCard (in OralCard protein View for A1AT, choose PDB in

left Menu, find the structure corresponding to ‘‘Crystal

Structure of a Serpin:Protease Complex’’ and click ‘‘Down-

load’’) (Fig. 6) and analysed with the PyMOL software.87

We identified several possible sites of non-enzymatic

glycosylation on inhibitor amino acids near the point of

attachment to the active site of the enzyme. These non-

enzymatic glycosylations can prevent serpins inhibitory

function, since the binding of the inhibitor to the serine

enzymes involved in the coagulation cascade is prevented,

allowing the hypercoagulate state characteristic of diabetes,

even in the presence of elevated levels of protease inhibitors of

the coagulation cascade.

OralCard also stores information on proteins from micro-

bial sources. However, experimental studies using oral

samples from DMT2 patients have not been able to identify

any proteins of microbial origin, which precludes any

conclusions as to the role microbial proteins might have in

the molecular mechanisms of oral imbalances in DMT2

patients.

With DMT2 it was possible to illustrate how OralCard can

be used to explore information from different sources,

enabling the user, in a few clicks, to get information on the

proteomic evidence of alterations in the oral cavity of diabetic

patients. In this case, biological processes such as blood

coagulation were known to be altered by clinical evidence/

studies. OralCard, in a simple manner, allowed verification of

the proteomic data available, revealing evidence of which

proteins are really expressed in altered quantities and

identification of the structural reasons for the lack of function

of these specific proteins. This tool allowed the extraction of

biological meaning from the published proteomic results by

revealing the molecular evidence that can explain the

impaired healing of oral tissues in diabetic patients, as well

as key molecules in the process. In other cases, the researcher

might not have clues as to which processes are altered in

pathology, but OralCard can be used in a systematic approach

to search for the proteomic evidence of altered pathways in

the disease and then to analyse the proteins involved therein.

The main limitation of these approaches is that most

proteomic studies of oral samples in several diseases report

only the presence or absence of a protein without protein level

quantification. This fact makes the search for altered targets

more time-consuming for there is no indication as to which of

the possible altered proteins varies the most. As more

quantitative proteomics studies are published, they will be

included in OralCard and interpretation of proteomics data

from the oral cavity will become progressively easier and more

accurate. For all the reasons presented, OralCard is a key tool for

the design of experimental work in quantitative proteomics.


One other aspect we became aware of during manual

curation of the information present in OralCard, is that there is

still some variability of the proteomic data generated by the

sampling methods and the proteomic techniques used. These

aspects have been identified by other authors,88,89 who

reported the need for standardization in saliva and oral tissue

sampling, processing and proteomic analysis. Recently,

studies have been published on the rational for protocol

standardization90 and it is our expectation that these studies

will result in more reproducibility in proteomic experiments

performed in different laboratories.

Funding

This work was supported by the European Community’s

Seventh Framework Programme (FP7/2007–2013), under grant

agreement no. 200754 (GEN2PHEN project), and from Funda-

cao para a Ciencia e Tecnologia, FCT, under grant agreement

PTDC/EIA-CCO/100541/2008. Joel P. Arrais is funded by FCT

grant SFRH/BPD/79044/2011.

Competing interests

None declared.

Ethical approval

Not required.

Authors’ contribution

All authors contributed extensively to the work presented in

this paper. NR, DA and MJC were the main responsible for the

bibliographical review and testing. JPA and JM contributed to

the modelling and development. MB and JLO were responsible

for the work supervision. All authors discussed the results and

implications and contributed to the manuscript.

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OralCard: A bioinformatic tool for the study of oral proteome

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