AtPIN: Arabidopsis thaliana Protein Interaction Network
© Brandão et al; licensee BioMed Central Ltd. 2009
Received: 16 July 2009
Accepted: 31 December 2009
Published: 31 December 2009
Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets.
All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3) which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW) calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS) (AT5G26710) we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630), a disease resistance protein (AT3G50950) and a zinc finger protein (AT5G24930), which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening.
AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.
Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. Recently, many experimental procedures have been developed to help elucidate the intricate networks of PPIs ranging from high-throughput experiments based on genomic scale analyses [1–4] to molecular biology approaches on a specific key pathway [5–7]. Sometimes the costs (financial and personal) of such exploratory experimental approaches are prohibitive; to circumvent this drawback, the bioinformatics alternative is frequently used as a valuable preliminary step to point to a more specific target, reducing both costs and time.
All of the protein-protein interaction information is often made freely available on different public databases with searching tools commonly restricted to one specific data set. However, even using standard formats to exchange data such as Molecular Interaction XML Format (PSI MI XML)) protein nomenclature may differ, impairing comparisons among databases without some protein name conversion.
Some authors make use of methodologies such as yeast two-hybrid, mass spectrometry, immunoprecipitation, or fluorescence resonance energy transfer assays to demonstrate protein interactions [9–14]. But, in some cases, protein interaction networks were determined solely by bioinformatics tools [15–18], and were not confirmed by experimental methodologies. In addition, those predictions rarely consider the subcellular localization of the interactors. The function of a protein is governed by its interaction with other proteins inside a cell, but even if two proteins are consistently predicted to interact they must be located at the same cell compartment and at the same time.
Arabidopsis thaliana has long been used as a model organism in a wide range of protein function, interactions and mutational studies . Thus, a lot of predicted and curated data is now available on centralized databanks such as TAIR  or throughout scientific literature. In this work, we present the Arabidopsis thaliana Protein Interaction Network (AtPIN), a database that integrates five available interaction data sets and two other databases: SUBA, a subcellular localization database [21, 22] and TAIR gene ontology and annotation . We also generated a web interface to query AtPIN and built the networks in a Cytoscape  easily importing format (XGMML and SIF).
One of the AtPIN key points is its integrative profile, queries response encompass experimental and predicted information on the protein interactions as the subcellular location and its database structure flexibility, facilitating the addition of new data sets, as well as additional analyses parameters. AtPIN presents some advantages upon other available systems: it is specific for A. thaliana protein interaction; the scoring system for co-localization; easily integration with Medusa  and Cytoscape  for PPI network visualization and manipulation.
Construction and content
AtPIN database (AtPINDB)
We used MySQL http://www.mysql.com/ to build AtPINDB due to its transactional SQL database engine and fastness. AtPINDB integrates more than 96,000 PPIs (96,221 as in release 8) from five public available databases: IntAct [26, 27], BioGRID , Arabidopsis protein-protein interaction data curated from the literature by TAIR curators [20, 29], the Predicted Interactome for Arabidopsis , and the A. thaliana Protein Interactome Database (AtPID) , all of them are queried weekly for updates.
The PPIs demonstration methodologies on AtPINDB were divided into two categories: Experimental: This means that the indicated PPI was experimentally demonstrated using Arabidopsis thaliana proteins. Predicted: The indicated PPI was proposed based on ortholog studies.
All interaction updates are locally curated, manually and automatically via a homemade set of PERL scripts and performed as follows: 1) If necessary, change the protein identification to TAIR locus name, based on conversion data available at the TAIR website ftp://ftp.arabidopsis.org/home/tair/Proteins/Id_conversions/; 2) update all annotation and gene ontology information to the most current available at TAIR ftp://ftp.arabidopsis.org/home/tair/Ontologies/Gene_Ontology/. 3) update the subcellular information for each locus based on SUBA . 4) update all interactions from databases. Experimentally demonstrated interactions have priority over predicted ones, and once the PPI status is updated its Pubmed links will now represent the direct evidence publication as well as the experimental method used to demonstrate this interaction. 5) Check and update the experiment controlled vocabulary. All experimental data is present in a controlled vocabulary based on the Molecular Interactions from Proteomics Standards Initiative (PSI_MI)) available at http://www.berkeleybop.org/ontologies/obo-all/psi-mi/. 6) Recalculate the cellular compartment classification and FSW as described below.
Cellular Compartment Classification
exp = Experimentally demonstrated, pred = indicated by prediction and local = specific subcellular location
The last class is Unknown: which indicates that there is no available data to calculate the C3 value or the data does not fit onto any class previously described. It is noteworthy that C3 value is an active characterization due to its dependency on experimental data availability of protein interaction as well as subcellular location.
Another probability shown by AtPIN is the PEP. This is a Bayesian probabilistic score calculated based on all data available in AtPINDB so, it is dependent on the availability of experimental data. It is represented by two values, first the probability of a particular PPI be experimentally demonstrated once it was predicted, and second, same as state for the first but of both interactors were experimentally co-localized, for the release 8 those values are 2.6% and 9.0% respectively. The PEP value is unique for each AtPINDB release, an updated value is shown at website, and should be used only as a statistical evaluation of AtPINDB.
Functional similarity weight
It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function [31–35]. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. Two related mathematical approaches, CD-distance  and FSWeight , have been proposed to assess the reliability of protein interaction data based on the number of common neighbours of two proteins. Both were initially projected to predict protein functions, and lately have been shown to perform well for assessing the reliability of protein interactions . Wong  have shown that using FSWeight, which estimates the strength of functional association, to remove unreliable interactions (low FSWeight) improves the performance of clustering algorithms.
The pairs of interacting proteins that are highly ranked by this method are likely to be true positive interacting pairs. Conversely, the pairs of proteins that are lowly ranked are likely to be false positives. The most interesting feature of the CD-distance and FSWeight is that they are able to rank the reliability of an interaction between a pair of proteins using only the topology of the interactions between that pair of proteins and their neighbors within a short radius in a graph network [32, 38].
N avg = Average of interactions made by each protein in AtPINDB.
The effectiveness of using FSWeight as a PPI reliability index was demonstrated using 19.452 interactions in yeast obtained from the GRID database , over 80% of the top 10% of protein interactions ranked by FSWeight have a common cellular role and over 90% of them have a common subcellular localization [32, 38]. In AtPIN (release 8 of AtPINDB), using the same top 10% of protein interactions ranked by FSWeight, we show that 59% PPIs share the same sub-cellular compartment and 83% have the same function or participate in the same cellular process. A good FSWeight value threshold starting point is the top 20%, since Chua  and Chen  have demonstrated that a protein pair having a high FSWeight value, above this value, are likely to share a common function. We have made available on the AtPIN website a table with live calculation of top ranked FSWeight values ranging from the top 1% to the top 99% showing the percentage of PPIs that share the same sub-cellular localization and function, as well as the FSWeight cut off value.
Utility and discussion
We present two study cases, first encompassing the aminoacyl-tRNA synthetases (aaRS), a de novo experiment, and, a second found in literature, using the phytochromes proteins.
Proteins identified by AtPIN as interactors with Glutamyl-tRNA Synthetase (AT5G26710)
Elongation factor 1B-gamma, putative/eEF-1B gamma, putative
Serine/threonine protein phosphatase 2A (PP2A) regulatory subunit B', putative
NRPA2 (nuclear RNA polymerase A 2); DNA-directed RNA polymerase
Ran-binding protein 1 domain-containing protein/RanBP1 domain-containing protein
HTA9; DNA binding
ATG12a (AUTOPHAGY 12); protein binding
Elongation factor 1B-gamma, putative/eEF-1B gamma, putative
ATRPAC43 (Arabidopsis thaliana RNA polymerase I subunit 43); DNA binding/DNA-directed RNA polymerase
Replication factor C 40 kDa, putative
DEAD/DEAH box helicase, putative
Protein kinase, putative
Coatomer protein complex, subunit beta 2 (beta prime), putative
IRE1A (Yeast endoribonuclease/protein kinase IRE1-like gene); kinase
Protein kinase family protein
UTP--glucose-1-phosphate uridylyltransferase family protein
CYT1 (CYTOKINESIS DEFECTIVE 1); nucleotidyltransferase
Transducin family protein/WD-40 repeat family protein
Zinc knuckle (CCHC-type) family protein
OMR1 (L-O-METHYLTHREONINE RESISTANT 1); L-threonine ammonia-lyase
60S ribosomal protein L37a (RPL37aB)
APG12/APG12B (AUTOPHAGY 12)
Ran-binding protein 1 domain-containing protein/RanBP1 domain-containing protein
Serine/threonine protein phosphatase 2A (PP2A) regulatory subunit B', putative
Disease resistance protein (CC-NBS-LRR class), putative
EIF4G (EUKARYOTIC TRANSLATION INITIATION FACTOR 4G)
60S ribosomal protein L37a (RPL37aC)
MEKK1 (MYTOGEN ACTIVATED PROTEIN KINASE KINASE); DNA binding/kinase/kinase binding
Methionine--tRNA ligase, putative/methionyl-tRNA synthetase, putative/MetRS, putative
Disease resistance family protein/LRR family protein
KH domain-containing protein NOVA, putative
SEC10 (EXOCYST COMPLEX COMPONENT SEC10)
Peptidase M1 family protein
ATRAD51 (Arabidopsis thaliana Ras Associated with Diabetes protein 51); damaged DNA binding
Zinc finger (B-box type) family protein
GS2 (GLUTAMINE SYNTHETASE 2); glutamate-ammonia ligase
ATRAD3 (ATAXIA TELANGIECTASIA-MUTATED AND RAD3-RELATED); inositol or phosphatidylinositol kinase
ETFBETA; electron carrier
KH domain-containing protein
GTP-binding family protein
CNX5 (SIRTINOL RESISTANT 1); Mo-molybdopterin cofactor sulfurase
DNA helicase, putative
Phytochromes are dimeric chromoproteins that regulate plant responses to red (R) and far-red (FR) light. Recently, Clark and co-authors  characterized the dimerization specificities of the Arabidopsis phytochromes in yeast two-hybrid analyses and by coimmunoprecipitation (co-IP), and demonstrated that two phytochrome forms, phyC (AT5G35840) and phyE (AT4G18130), do not homodimerize and, instead, heterodimerize with phyB (AT2G18790) and phyD (AT4G16250). Interestingly, the phyE heterodimeriziation with phyD was previously predicted by two different data sets present in AtPINDB and no homodimerization were predicted.
This observation shows that AtPIN might be a useful, additive and reliable strategy to map protein-protein interactions in Arabidopsis, once it integrates a wide range of PPIs from different sources.
AtPIN is a user-friendly tool to aggregate information on Arabidopsis thaliana PPIs, ontology, and subcellular localization. This database may help in elucidating the intricate network of A. thaliana protein interactions. The AtPIN usability is aimed at new researchers as well as more skilled personnel. The XGMML and SIF file generation may help in the construction of more complex PPI networks with no previous computer language knowledge since these files can be easily merged and edited.
Availability and requirements
The AtPIN web server is publically accessible via Http://bioinfo.esalq.usp.br/atpin. To take full advantage of the AtPIN system, a user's web browser should support AJAX and JAVA. All data downloaded from the AtPIN server are tab-delimited ASCII format.
The authors are grateful for the helpful comments of an associate editor and anonymous referees. We thank Prof. Yang Zhang for critical discussion on AtPIN and to Prof. Antonio Augusto Franco Garcia for statistical discussion during AtPIN production. To Christine Stock for critical reading of this manuscript, Raj Ackbul for help on debug Perl scripts and MySQL. Funding: This work was supported by grants from CNPq (151048/2007-0) and FAPESP (05/54618-9), Brazil. M.M.B. is a post-doc fellow funded by CNPq. L.L.B.D. has a graduate scholarship from CNPq. M.C.S.F. is also a research fellow of CNPq.
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