A new approach based on similarity in codon usage is used to predict protein-protein interactions. We introduce a novel approach to predict interaction of two proteins solely by analyzing their coding sequences. We found that similarity in codon usage is a strong predictor of protein-protein interactions and, for high specificity values, is as sensitive as the most powerful current prediction methods. Furthermore, combining codon usage with other predictors results in a 75% increase in sensitivity at a precision of 50%, compared to prediction without considering codon usage. Saccharomyces cerevisiae proteins. Hence, a method to identify protein-protein interactions solely on the basis of gene sequences would significantly expand the ability to predict interaction networks.The need to transform the growing amount of biological information into knowledge has involved several disciplines that, by means of experimental and computational approaches, aim to decipher functional linkages and interactions between proteins ,2. Curre6 false positives. These studies consider protein sequences, and ignore the plethora of information that exists in their coding sequences. The still-unsatisfied demand for reliable sequence-based prediction of protein-protein interactions encourages exploration of relevant sequence features in the genome instead of the proteome.A few studies have been performed on the prediction of protein-protein interactions based only on amino acid sequence information -8. HowevIt has been widely acknowledged that codon usage is correlated with expression level . In addiIn this paper, we show that codon usage of functionally and/or physically linked proteins in an organism contain enough information to enable us to detect these proteins, even in the absence of homologues in other organisms. Furthermore, we show that our method is several times more sensitive than tracking the coordinated changes of codon usage over different organisms, and in fact is one of the best methods for identification of protein-protein interactions.S. cerevisiae, Escherichia coli and Plasmodium falciparum. S. cerevisiae is a eukaryote with moderate coding G+C content (39.77%), while the genome of P. falciparum has an extremely low coding G+C content (23.8%), and E. coli is a prokaryote with moderate coding G+C content (52.35%). For each organism, a positive and a negative gold standard set of protein pairs were defined, where a positive gold standard set comprises open reading frame (ORF) pairs that, based on previous reports, encode proteins that interact with each other , and a negative set consists of ORF pairs whose products do not interact with each other (Table Here we consider three different organisms: er Table . It shoufi(c) and fj(c) are relative frequencies of codon c in ORF i and ORF j, respectively (Σkfi(ck) = 1 and Σkfj(ck) = 1; k = 1,2,..64 indicates all 64 codons). Therefore, dij demonstrates the distance of two ORFs in terms of usage of codon c. We found that for almost all codons, distribution of d differed between positive and negative gold standard sets . Generally, distribution of d shifts to smaller values for ORFs within the gold standard positive set, indicating that interacting ORFs are more similar in codon usage profile than non-interacting ORFs. However, this shift is marginal for each codon individually, which means that single codons are weak predictors of protein-protein interactions.where d for each codon into 50 intervals, for each of which we calculated the likelihood ratio, that is, the fraction of positive gold standards occurring in that interval divided by the fraction of negatives occurring in that interval. Since the mutual information of d for each pair of codons was negligible, we combined these likelihood ratios using a naïve Bayes approach . Although obviously not all features were independent from each other , we found that a naïve Bayesian network is more effective than a Bayesian network in which each variable node has one other parent node, perhaps because the increase of the parameters in the latter case causes partial overfitting of the network. Using a tenfold cross-validation method, we evaluated the performance of this naïve Bayesian network in predicting protein-protein interactions. To do so, we divided the gold-standard set into ten random segments; each time we used nine segments as the training set and calculated the combined likelihood ratios for each ORF pair in the remaining segment. We designate the method 'PIC' (for probabilistic-interactome using codon usage).We divided the distribution of S. cerevisiae, P. falciparum and E. coli. For all three organisms, codon usage is a strong predictor of protein-protein interactions. As an extremely G+C poor parasite with a highly biased codon usage . nt study  has showfication . Based o2000 predicts six new interactions between RNase P complex and other proteins in yeast, neither of which exists in PIT-Lcut600 or has been reported previously. Four interactions are with uncharacterized ORFs, YKL096C-B, YDL159W-A, YKL183C-A and Q0255. Q0255 is likely to code for a maturase-like protein. It has been hypothesized that mitochondrial maturases participate in splicing by stabilizing some secondary or tertiary structure needed for splicing  < 0.01 was assumed not to influence the performance of the naïve Bayesian network. To combine two probabilistic networks, we multiplied the likelihoods each network assigned to each interaction.We used ck) (see ). For PI network  is emploormation , where Iet al. [S. cerevisiae, Saccharomyces paradoxus, Saccharomyces mikatae, and Saccharomyces bayanus [S. cerevisiae [E. coli in the analysis did not improve the results. We did not add more genomes because we would lose a portion of our gold standard sets, especially the negative gold standard set, due to the lack of homology for all genes among all genomes, resulting in non-comparable sensitivity/specificity values.We performed the same analysis as described by Fraser et al. , using t bayanus . We usedrevisiae  to identCAI, codon adaptation index; EF, elongation factor; HMG, high mobility group; HUB, Histone mono-ubiquitination; KEGG, Kyoto Encyclopedia of Genes and Genomes; Lcut, likelihood cutoff; MIPS, Munich Information Center for Protein Sequences; ORF, open reading frame; PI, probabilistic interactome; PI-A, PI using amino acid frequencies; PIC, probabilistic-interactome using codon usage; PICT, combination of PIC and PIT; PIE, PI-experimental; PI-L, PI using sequence length; PIP, PI-predicted; PI-RSCU, PI using RSCU; PIT: PI-total.HSN and RS contributed to all aspects of this research. Both authors read and approved the final manuscript.d for each codon in yeast. Additional data file 600 and PIC-Lcut600. Additional data file 600 and PIC-Lcut600 for S. cerevisiae. Additional data file 600 for S. cerevisiae. Additional data file 2000 for S. cerevisiae. Additional data file P. falciparum gold standard set.The following additional data are available with the online version of this paper. Additional data file d for each codon in yeast.Distribution of Click here for fileComparison of the naïve Bayesian network and fully connected Bayesian network in the yeast gold standard positive set.Click here for fileComparison of the naïve Bayesian network and fully connected Bayesian network in the yeast gold standard negative set.Click here for fileVariance over different components resulting from principal component analysis of the interacting gene pairs in yeast.Click here for fileComparison of PIC, PI-RSCU, PI-A and PI-L.Click here for fileComparison of PIP × PIC and the yeast gold standard positive set.Click here for filePer-complex comparison of PIP and PIP × PIC.Click here for file600 and PIC-Lcut600.MIPS functional category enrichment for the yeast genome, PIP-LcutClick here for file600 and PIC-Lcut600 for S. cerevisiae.Distribution of mRNA expression levels in interactions predicted by PIP-LcutClick here for fileprecision-recall curves for PIC, interolog mapping (INT), phylogenetic profiles (PGP), Rosetta stone (ROS), CAI coevolution (co-CAI) and CAI.Click here for filePrecision-recall curves for PIC, PIT and PICT.Click here for file600 for S. cerevisiae.PIC-LcutClick here for file2000 for S. cerevisiae.PICT-LcutClick here for fileP. falciparum gold standard set. For the performance of PIC on Escherichia coli gold standard set check reference [Results of performance of PIC on the eference .Click here for file


PSAIA  was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites.In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm) for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA.Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results.PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format.In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis.Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites. Analysis performed on small data sets have shown  that geoIt is also important to generate results that can be easily formatted for statistical analysis with standard software. Current software for protein analysis  typically investigates a single aspect of molecular structure geometry and generates results only as simple text tables. These tables, whilst easily readable, are not ideal for parsing by computer.Tools for determining protein-protein interactions are very scarce. Also, current software tools for structural property calculations from protein geometry ' and Table PSAIA was designed both to simplify the use of algorithms for the analysis of sets of Protein Data Bank (PDB)  files imPSAIA can process each chain within a given molecular structure file separately . This feature usefully allows easy investigation of structure features before and after association (in bound and unbound form), but is rarely offered in other software tools. Instead, users are required to create a separate PDB file for each chain they wish to investigate individually.iPFAM [Four protein-protein interaction analysis algorithms are currently offered to the user by PSAIA. A strong feature of PSAIA is that it allows these algorithms to be used in combination with a selection of structure analysis methods. Whilst valuable, information provided by specialised databases, e.g. iPFAM  represenPSAIA supports two output formats: a textual table, for reading directly, and XML (Extensible Mark-up Language) for parsing for further statistical analysis.PSAIA can process nucleotide as well as peptide chains from PDB, although currently no interaction parameters are calculated for nucleotide chains.PSAIA consists of two separate tools: the PSA  and the PIA . The software is designed to function in both Linux and Windows operating systems, either as a command-line tool or through a standalone Graphical User Interface (GUI). The software is written in C++ and the GUI uses the QT Library 4.1.4 . PSAIA uPSAIA verifies all input files format including configuration and PDB files. The input filter accepts "null" chains (chains without identification character) in PDB files. From summer 2007, PDB files with "null" chains will not appear in new releases. PSAIA accepts "null" chains for backward compatibility.In case of missing parameters  PSAIA will issue a warning to the user at the end of processing. In case of incorrect PDB or configuration file formats, PSAIA will stop execution and display a warning message.Depth Index – DPX [Protrusion Index – CX [Solvent Accessible Surface Area – ASA [Relative Solvent Accessible Surface Area – RASA [Geometry calculation algorithms included in PSAIA work primarily at the atomic level, with residue attribute values derived by averaging atomic measurements. The following geometry parameters can be calculated: ex – DPX , Protrusdex – CX , Solventea – ASA , Relativa – RASA . PSAIA aa – RASA .accessible surface area (ASA) is the atomic surface area of a molecule  that is accessible to a solvent, and is usually expressed in Å2 (square Ångstroms). ASA is calculated using the 'rolling ball' algorithm [The lgorithm , which uRelative ASA (RASA) attribute is the per-residue ratio between the calculated ASA and 'standard' ASA for a particular residue. Standard ASA values are usually determined by calculating ASA values of a central residue in a triplet (e.g. ALA-X-ALA) .For use within PSAIA, for each of the twenty standard residues, one thousand random triplets (with the desired residue in the middle) were selected from random PDB files. For each central residue within the extracted triplet, five ASA attributes (given above) were calculated and their mean values were determined. These mean values are taken as representative standard ASA attributes for each of the twenty residues, and are provided in a file within PSAIA distribution. However, the user has the possibility to provide alternative 'standard ASA' values as a separate input file. Standard ASA values were also calculated for nucleotides in the same manner as for amino-acid residues.The ASA and RASA parameters are calculated separately for each chain in the PDB file, as well as for all chains together. Compared to NACCESS there is a difference in calculation of backbone and side chain ASA of Glycine. PSAIA treats Glycine C-alpha atom as a part of the backbone, while NACCESS considers it a part of the side chain.For ASA and RASA, the following residue attributes are calculated:total – sum of all atom values.• backbone – sum of all backbone atom values.• side-chain – sum of all side-chain atom values.• polar – sum of all oxygen, nitrogen and phosphorus (for nucleic acids) atom values.• non-polar – sum of all carbon atom values.• i (DPXi) is defined as its distance (Å) from the closest solvent accessible atom j :The depth of an atom d1, d2, d3, ..., dn are the distances between the atom i and all solvent accessible atoms. The depth (DPX) is thus zero for solvent accessible atoms, and >0 for atoms buried in the protein interior, with deeply buried atoms having higher DPX values [where X values .int. The remaining volume of the sphere, Vext, is calculated as the difference between the volume of the sphere and Vint. The CX value is then defined by Vext/Vint [The principle of CX algorithm is as follows. For each non-hydrogen atom in protein structure, the program calculates the number of heavy atoms within a fixed distance R . The number of atoms within sphere is multiplied by the mean atomic volume found in proteins , which ext/Vint .For both DPX and CX the following residue attributes are calculated:total mean – mean value of all atom values.• total standard deviation.• side-chain mean – mean value of all side-chain atom values.• side-chain standard deviation.• maximum – highest of all atom values.• minimum – lowest of all atom values.• The following interaction algorithms are implemented in PSAIA (subprogram PIA):Atom Nucleus Distance – method introduced by Ofran and Rost [• and Rost .Atom Van der Waals Radii Distance – method introduced by Aytuna et al. [• a et al. .ASA Change on Complexation – method introduced by Jones and Thornton [• Thornton .• PIADA – a novel algorithm designed during PSAIA development.PIA calculates only protein-protein interaction. Protein-nucleic acid and nucleic-nucleic acid interactions are not included in the calculation.This very simple method defines two residues from the opposite chains as interacting if there is at least one pair of non-hydrogen atoms, one from each residue, at a distance below the specified threshold. Values for the threshold are usually 4.5 – 6 Å.2. It is important to emphasize that this method can only define interaction residues, but not their interacting partners.In this method, ASA is calculated for a particular residue before and after the process of complexation. If the difference between ASA in unbound and bound form is above the specified threshold, then a residue is defined as an interacting residue. The usual value for threshold is 1 ÅTwo residues from the opposite chains are marked as interacting if there is at least one pair of non-hydrogen atoms, one from each residue, at a distance smaller than the sum of their van der Waals radii plus a threshold. Thresholds are usually 0.5 – 1.5 Å.Although algorithms that use atomic distance cut-off for definition of interaction exist, they are either too general ,19 or doOur algorithm for detecting the protein-protein interactions calculates four different types of interactions: ionic, Van der Waals, hydrophobic and polar. Table Hydrophobic interactions are defined as interactions between any two of the seven non-polar amino acids  when the following condition is fulfilled: distance between residues, measured as distance between any two atoms from different residues, is less than 4.7 Å.Van der Waals interactions are defined as interactions between any two amino acids when the distance between residues (non-hydrogen atoms) is less than the sum of their Van der Waals radii plus 1.125 Å. The value of 1.125 Å has been determined empirically by comparing our results with results from the iPFAM database [database .There are two main tabs in the interface: the structure analyser and the interaction analyser. Figure Prior to analysing a structure, the user should predefine the following files:• A file with total, backbone, side chain, polar and non-polar ASA for particular amino acid residue and nucleotides.• A file with value of hydrophobicity for each of amino acid residues.• A file with Van der Waals Radii for each atom of particular residue and nucleotide, ligand atoms and hetero atoms.All of the files mentioned above are included in the installation package with common values, but users can easily change them or add some values if it is necessary for their analysis. The software memorises the location of the last configuration files and uses them as default for the next run.Structure algorithms', while with z-slice value users can define precision of calculation. It is recommended to use default values.The user can optionally change ASA attributes (z slice size and solvent probe radius)  and protUpon predefining parameters, the user should select the following: output directory attributes for analyses , chain bonding type (bound and/or unbound) and output data format (text table and/or XML). Example output from structure analyzer is given in Additional files The final step is the selection of protein files. Files can be added manually or from a predefined list file. PDB files should be correctly formatted or the software will return an error message.There are two additional options: saving the already selected files in a list file and creating a configuration file based on selected parameters and definition files. Both of these options can also be used for a console version of application for massive analysis on the remote server.Interaction analyser gives the user a possibility to use one of the four interaction calculation algorithms mentioned above . Like in the structure analyser, the user should first specify parameters and configuration file. The only configuration file is Van der Waals Radii file. In accordance with the selected algorithm, the user can choose threshold distance  or delta ASA threshold value  between There are two console applications: PSA  and PIA . In order to use both of them, it is necessary to define the configuration file and a file with the list of proteins. These files can easily be created by GUI version of the application.PSAIA is a software tool that integrates several algorithms for protein interaction and structure geometry analysis of protein complexes into a single application. The software can be used via a graphical user interface or from the command-line, and user can choose between a tabular and XML output formats. Protein structure and protein interaction data are in many cases investigated together, and respective algorithms often share the same software code. Therefore, it was an obvious choice to combine the two analysis types into a single application.The main aim of PSAIA is to analyse and predict both protein structural features and interaction sites. XML formatted output simplifies conversion of results into any other desired format. GUI and command-line versions for MS Windows and Linux platform make the software accessible to a broader range of potential users. Furthermore, the console application enables the execution of large scale analyses on remote servers.Results for each of the two integrated tools (PSA and PIA) are presented separately.1LFD). The obtained values , and PIADA algorithm of PIA ran for an average of 42 seconds (0.1 s per protein).The interaction analyser consists of three pre-existing algorithms for calculation of interacting residues plus the newly designed PIADA which determines the interaction between particular residues according to their type and the type of interacting atoms as described in the implementation section. A similar algorithm is used for creating iPFAM database . HoweverTo test the new algorithm, we compared the number of interactions obtained by PIADA on a set of 2 445 protein complexes with the results obtained by the distance based algorithm (with threshold distance of 4.5 Å) and data from the iPFAM database .Total numbers of interacting residues found by a particular algorithm are summarised in Table iPFAM). PIADA found 88 % of the interaction residues found by the ASA based algorithm. The main difference between these two methods lies in different definitions of interaction (distance based and space based). Also, delta ASA (difference between ASA in unbound and bound form) threshold for interaction is taken as 1 Å2 [Interacting residues detected by the atom distance algorithm overlap with PIADA results in 97 percent of instances. The observed difference occurs mostly in the 'weak Van der Waals' interactions, i.e. those with inter-atomic distance under 4.5 A, but above the Van der Waals threshold . We successfully tested the method on a test set of 1387 PDB structures prepared by PSAIA.Moreover, PSAIA is of great help as a verification tool in docking studies. For example, we use it for the calculation of changes in ASA as well as for a quick scan of interacting residue pairs in docked structures.In the next version we plan the following improvements to the software: increasing the number of available algorithms and improving implementation of the existing ones in terms of speed; program parallelisation and support for other input data formats. Protein secondary structure is an important factor for studying protein interactions and therefore we plan to include one of the secondary structure calculation algorithms into the program PSAIA is designed for analysis of a large number of molecular structures using PDB files as input data. Further improvement in speed and processing capability of the program will be parallelisation of the analysis process by distributing one structure per processor. The next release of the PIADA algorithm will include the possibility to investigate interactions between molecules of any type, including hetero atoms and nucleic acids. Additionally, mmCIF and XML file formats are now widely used for storing molecular structure information in the Protein Data Bank, and the next program version will include the possibility to process these file formats.PSAIA can process a series of PDB files and calculate a large number of protein structure parameters as well as determine interaction residues based on several different algorithms. In addition PSAIA can calculate parameters for protein chains in unbound and bound form.In addition to established algorithms, PSAIA offers our new PIADA algorithm for calculation protein interaction residue pairs. Results obtained from PIADA correlate well with, and generally improve upon, the results of the alternative algorithms. PIADA offers the advantage that it can be also used to define the type of protein interaction and to execute a quick scan of interacting residue pairs in docked structures.Uniquely, PSAIA allows simultaneous application of so many different algorithms for structure analysis and for determination of interacting residue pairs. XML output enables an easy conversion of data to formats suitable for statistic analysis using standard software. Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.Project name: PSAIA• Home page: • Operating systems: MS Windows, Linux• Programming language: ANSI C++• Other requirements: Qt 4.1.4 for Linux• License: GNU GPL• Availability: Additional file • Restrictions for the use by non-academics: None• PSAIA: Protein Structure and Interaction Analyzer; PSA: Protein Structure Analyzer – console application; PIA: Protein Interaction Analyzer – console application; PDB: Protein data bank; ASA: Accessible surface area; CX: Protrusion index; DPX: Depth index; MPI: Message Passing Interface; PVM: Parallel Virtual Machine; GUI: Graphical User Interface; XML: Extensible Mark-up LanguageJM contributed to design, developed software and has been involved in manuscript drafting. MS contributed to design, had made intensive testing of software and was involved in manuscript drafting. ST contributed to design of PIADA algorithm, and was involved in revising the manuscript. BJ contributed to design and was involved in revising the manuscript. KV contributed to design and was involved in revising the manuscript. All authors read and approved the final manuscript.1LFD – output of PSAIA Structure analyser in table form. This file includes information about ASA, DPX, CX and hydrophobicity values per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in bound form.PDB:Click here for file1LFD – output of PSAIA Structure analyser in XML form. This file includes information about ASA, DPX, CX and hydrophobicity values per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in bound form.PDB:Click here for file1LFD binding residues – output of PSAIA Interaction Analyser in table form. This file includes information about residues that are included in interaction in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for file1LFD binding residues – output of PSAIA Interaction Analyser in XML form. This file includes information about residues that are included in interaction in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for fileXML Schema for binding residues. This is the XSD schema definition for XML output file  of a particular residue in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for file1LFD binding status – output of PSAIA Interaction Analyser in XML form. This file includes information on interaction status (in interaction or not in interaction) of a particular residue in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for fileXML Schema for binding status. This is the XSD schema definition for XML output file (Additional file Click here for file1LFD contacts – output of PSAIA Interaction Analyser in table form. This file includes information on interaction partners in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for file1LFD contacts – output of PSAIA Interaction Analyser in XML form. This file includes information on interaction partners in PDB:1LFD obtained by maximum distance algorithm from PSAIA application.PDB:Click here for fileXML Schema for residue contacts. This is the XSD schema definition for XML output file.Click here for fileXML Schema for peptide structure in bound form. This is the XSD schema definition for XML output file.Click here for fileXML Schema for peptide structure in unbound form. This is the XSD schema definition for XML output file.Click here for file1LFD – output of DSSP application. This file includes information about secondary structure and total ASA per residue of PDB:1LFD obtained from DSSP application.PDB:Click here for file1LFD – output of NACCESS application. This file includes information about total, relative, backbone, side-chain, polar and non-polar ASA per residue of PDB:1LFD obtained from NACCESS application.PDB:Click here for file1LFD – CX output of PSAIA Structure Analyser. This file includes information about CX per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in unbound form.PDB:Click here for file1LFD – output of CX server . This file includes information about maximum, minimum and average CX values per residue of PDB:1LFD obtained from CX server.PDB:Click here for file1LFD – output of dpx server . This file includes information about maximum, minimum and average CX values per residue of PDB:1LFD obtained from CX server.PDB:Click here for filePSAIA – MS Windows setup file. Setup file for installation PSAIA, PSA and PIA. Newer versions of the application can be downloaded from the project web site.Click here for filePSAIA – Linux installer. PSAIA installer for a x86 Linux platform. Linux installers for other architectures as well as the source code can be downloaded from the project web site.Click here for file


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.3) 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.All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (CArabidopsis 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.AtPIN is a friendly and easy-to-use tool that aggregates information on  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 -4 to molAll 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 Some authors make use of methodologies such as yeast two-hybrid, mass spectrometry, immunoprecipitation, or fluorescence resonance energy transfer assays to demonstrate protein interactions -14. But,Arabidopsis thaliana has long been used as a model organism in a wide range of protein function, interactions and mutational studies [Arabidopsis thaliana Protein Interaction Network (AtPIN), a database that integrates five available interaction data sets and two other databases: SUBA, a subcellular localization database [ studies . Thus, a studies  or throudatabase ,22 and Tdatabase . We alsodatabase  easily iA. thaliana protein interaction; the scoring system for co-localization; easily integration with Medusa [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 h Medusa  and Cytoh Medusa  for PPI http://www.mysql.com/ to build AtPINDB due to its transactional SQL database engine and fastness. AtPINDB integrates more than 96,000 PPIs  from five public available databases: IntAct [Arabidopsis [A. thaliana Protein Interactome Database (AtPID) [We used MySQL : IntAct ,27, BioG: IntAct , Arabido: IntAct ,29, the bidopsis , and the (AtPID) , all of Experimental: This means that the indicated PPI was experimentally demonstrated using Arabidopsis thaliana proteins. Predicted: The indicated PPI was proposed based on ortholog studies.The PPIs demonstration methodologies on AtPINDB were divided into two categories: 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 [http://www.berkeleybop.org/ontologies/obo-all/psi-mi/. 6) Recalculate the cellular compartment classification and FSW as described below.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  on SUBA . 4) upda on SUBA  availabl3 value) is represented as classes and is calculated using simple mathematical parameters: type of interaction + co-localization + determination of subcellular localization . The value attributed for the type of interaction is 4 if it is based on experimental data, and 0 if there is no experimental data available (predicted); for co-localization we attribute score 2, otherwise we display score 0; If subcellular localization is based on experimental analyses we score 1, and 0 if predicted. Considering all possibilities we divided the PPIs in the AtPINDB into 5 classes: Class A (C3 = 7): The PPI and subcellular location have been shown to be experimentally demonstrated and both proteins are co-localized. Class B (C3 = 5): The PPI and subcellular location have been experimentally shown, however, the proteins were localized to different subcellular compartments. Class C (C3 = 3): Same as Class A but the PPI is based on prediction analyses. Class D: Same as Class A but subcellular location is based on prediction analyses. For this class the same mathematical methodology is used to calculate the C3 but the subcellular localization value is based on prediction methodology made by SUBA. For each location identified as Class D, AtPIN indicates the probability of this particular prediction to be correlated to experimental data at AtPINDB. The Plocal is a probabilistic value, thus, the higher Plocal indicates a higher probability of this particular protein been found at the predicted cellular compartment, according to the data available in AtPINDB derived from SUBA database. This posterior probability is demonstrated as:The cellular compartment classification  is defined as:In AtPIN, we implemented the FSWeight algorithm originally proposed by Chua . The funWhereNA = set of interaction partners of A; NB = set of interaction partners of B; λA, B is a weight to penalize similarity weights between protein pairs when any of the proteins has too few interacting partners and is calculated as:whereNavg = 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 80http://bioinfo.esalq.usp.br/atpin/. A TAIR locus name can be used to query AtPIN and the response page displays all interactions found in AtPINDB, as well as the C3 value, PEP, and optionally, subcellular location information and gene ontology. The queried interactions may be visualized and manipulated online using Medusa JAVA applet [AtPIN web interface was entirely built as a PERL script and locally hosted on a DELL Poweredge server at A applet , alternade novo experiment, and, a second found in literature, using the phytochromes proteins.We present two study cases, first encompassing the aminoacyl-tRNA synthetases (aaRS), a A. thaliana cytosolic glutamyl-tRNA synthetase (gluRS) (locus AT5G26710) we combined two experimental approaches. First, analysis of the AtPIN database identified 45 candidate proteins, all of the interactions proposed by prediction analyses , 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.Phytochromes are dimeric chromoproteins that regulate plant responses to red (R) and far-red (FR) light. Recently, Clark and co-authors  characteArabidopsis, once it integrates a wide range of PPIs from different sources.This observation shows that AtPIN might be a useful, additive and reliable strategy to map protein-protein interactions in 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.AtPIN is a user-friendly tool to aggregate information on 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 AtPIN web server is publically accessible via MMB planned, wrote and tested all the software. LLBD performed all yeast two-hybrid experiments. MCSF provided guidance during all phases of planning, designing, testing and implementing AtPIN. All authors contributed to the writing of the manuscript.


As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data.H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete.The proposed method is assessed both biologically and statistically over the genome of It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable to a genome-wide application. Drosophila genes downloaded from FlyBase (November 2006) Genome sequencing projects have deposited tremendous amounts of protein sequence data for a vast number of genomes, and as we move into the post-genomic era, it will be crucial to determine biological functions for all these encoded proteins. Currently, a substantial portion of most genomes is still unannotated in silico methods. The most established computational approaches to function detection primarily depend on homology matching to genes with known functions utilizing programs such as FASTA While experimental methods such as loss of function mutational analysis, RNAi, or targeted misexpression approaches have been very successful in identifying protein functions, they are labor intensive and time consuming. As a result, much of the genome-wide functional annotations are based upon With the ever-increasing accumulation of high-throughput protein-protein interaction data, a number of computational approaches have emerged to take advantage of these data for gene function prediction in silico methods to increase the fidelity of these functional annotations and to propose novel functions for currently uncharacterized proteins presents a major challenge to the life science community and will eminently aid the biological community as higher quality functional annotations are often used by scientists to generate new hypotheses and direct their research focus.Although most computational methods have shown great promise in function assignment, current methods still suffer from two major limitations. First, most function prediction algorithms can predict protein functions with 50%–75% accuracy, which may not be of practical use for biologists. Moreover, some methods use only several tens to hundreds of functional categories in the prediction process which resulted in more generic rather than specific functional assignments. Therefore, developing more effective Homo sapiens genome, and find that CSIDOP is capable of making molecular function predictions for human proteins with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories (the most specific terms in GO). In addition, CSIDOP is able to assign novel functional annotations for 181 previously uncharacterized proteins. Furthermore, we demonstrate that CSIDOP can complement current GO annotation by providing additional functional annotations for proteins that are already characterized by GO.In this paper, we describe CSIDOP, Cross-Species Interacting DOmain Patterns, a new method for protein function assignment based on the shared interacting domain patterns extracted from cross-species protein-protein interaction data. In an evaluation of the CSIDOP method we use protein-protein interaction data from the The CSIDOP method tackled the protein function determination problem by analyzing interacting domain patterns that are conserved across different species. A brief synopsis of the method is presented here with a more detailed description presented in “Under this hypothesis, if two PPI pairs contain a common interacting domain pattern, then proteins in the two pairs with similar modular domains are more likely to be associated with similar functions. For example, assume that there exist two PPI pairs: protein A interacts with protein B and protein C interacts with protein D. If proteins A and C contain the same modular domain X that interact with the modular domain Y in proteins B and D, then we conclude that the two PPI pairs share a common interaction domain pattern. Therefore, we extrapolate that proteins A and C are more likely to have similar functions, and the same applies to proteins B and D .C. elegans, nhr-67 [Swiss-Prot: Q9XVV3] and daf-21 [Swiss-Prot: Q18688] have been shown to interact C. elegans and ESR1 in human are annotated to the same function terms such as ligand dependent nuclear receptor activity, regulation of transcription, DNA dependent, DNA binding, and transcription factor activity. Analogously, daf-21 and HSP90AA1 were found to be annotated with the same function terms, ATP binding and protein folding.We first explored this concept for PPI pairs from different species and have observed evidence of this conservation of function between the PPI pairs. For example, in It is important to note that this method is fundamentally different from other protein interaction-based function detection algorithms where the function of a target protein is determined strictly by its interaction partners. Compared with existing methods, our method is distinctive in the following aspects: (i) protein functions are detected through the shared interacting domain patterns, (ii) the patterns are mined from the cross-species protein interaction data, and (iii) unknown proteins can be assigned to various functional categories in GO, in contrast to most other methods where proteins are assigned with a limited number of functional categories such as MIPS An essential issue concerning the protein function prediction problem is the assessment of method reliability. To evaluate the CSIDOP method, a set of protein interaction data is partitioned into two groups: (1) training data: PPI pairs where both proteins are annotated in the GO, and (2) testing data: PPI pairs with at most one protein annotated. The training dataset is used to extract interacting domain patterns. The test dataset, on the other hand, contains interaction pairs that have either one of the proteins uncharacterized or both unknown. Thus, we can assess the reliability of the CSIDOP method by determining how well it worked in function prediction for those GO-characterized proteins and predict functions for proteins that are currently not characterized in GO in the test dataset.H. sapiens. The collected human protein interaction data were separated exclusively into training and test datasets as described above. To train the CSIDOP method, we integrated protein-protein interaction (PPI) data from the organisms S. cerevisiae, C. elegans, and D. melanogaster, in addition to the large data set from H. sapiens. In order to assess the relative performance of our method, inferred functions of the H. sapiens proteins (by CSIDOP) were then compared to the known functions in the GO database, which we designate as the ‘true’ terms. Hence throughout this paper, the ‘true’ function terms of a protein refer to the known function terms of this protein listed in the GO. An exact match between a predicted term and the corresponding true GO term for a protein indicates a correct prediction; and wrong prediction otherwise.We chose to evaluate the method using proteins in H. sapiens proteins from PPIs with common domain patterns in the lookup table. Among the 618 predicted proteins, 437 had existing annotations in the GO database, and thus could be used to evaluate the CSIDOP method. Among the 437 proteins, 417 were assigned with correct functions by the CSIDOP (assigned functions have an exact match with the ‘true’ terms), i.e., the CSIDOP method had an accuracy of 95.42%  method by Schwikowski et al. After training, CSIDOP produced a lookup table of significant interacting modular domain patterns from interaction pairs in the training dataset see , where ef 95.42%  using 2,Generally, the MR algorithm assigns a protein with the most frequent function terms among its direct interaction partners. Assessing the MR algorithm on the same target dataset that we used in CSIDOP, MR made functional predictions with an accuracy of 59.50% . As for S. cerevisae proteome using a smaller number of functional categories. Schwikowski et al. Most existing methods have been evaluated on the in D. melanogaster, RPT1 [Fly-Base: FBgn0028687], is annotated with endopeptidase activity inferred from direct assay S. cerevisiae, YKL145W is also annotated with the function terms endopeptidase activity and ATPase activity.A protein often exhibits multiple molecular functions and its annotation in GO may therefore not be complete. For the 20 proteins with predicted functions that do not match with their ‘true’ terms, the differences between the predicted terms and the ‘true’ terms may be due to incompleteness of the GO annotations. Moreover, the CSIDOP may provide additional functional terms to existing proteins. For example, the Alpha-2-macroglobulin precursor [Swiss-Prot: P01023], was predicted by CSIDOP to be involved in protease inhibitor activity (GO:0030414), which is not among the current list of functions annotated in GO. Consistent with this prediction, alpha-2-macroglobulin is found to be a major human plasma protease inhibitor capable of inhibiting most endopeptidases tested To gain insight into the 20 proteins that were “incorrectly” annotated by CSIDOP, we analyzed the relationship between the predicted terms and their true GO terms. Moreover, we analyzed correlations between the predicted function terms and the ‘true’ terms. In GO, gene products can be associated with more than one term. Therefore, the correlation between two GO terms is defined based on the number of gene products in common H. sapiens proteins that are not currently described in the GO database. Some of these novel annotations can be supported with evidence provided by QuickGO, a web browser of gene ontology data maintained by the European Bioinformatics Institute H. sapiens gene POLA2, DNA polymerase subunit alpha B [Swiss-Prot: Q14181], was predicted by CSIDOP to exhibit alpha DNA polymerase activity (GO:0003889). Orthologs of POLA2 found by Inparanoid include: POL12  in S. cerevisiae, POLA2 [RGD:621817] in R. norvegicus, and CG5923 [FlyBase: FBgn0005696] in D. melanogaster. All three orthologs were associated with the alpha DNA polymerase activity (GO:0003889).Importantly, the CSIDOP predicted functional annotations for 181 D. melanogaster CG5814 [FlyBase: FBgn0015625] shared both functional annotations, which were inferred from sequence or structural similarity and physical interaction Furthermore, the CSIDOP method detected three molecular function terms for the human protein SLY, SH3 protein expressed in lymphocytes homolog [Swiss-Prot: O75995], while no information was found anywhere else. The three functions identified were DNA binding (GO:0003677), chromatin binding (GO:0003682), and zinc ion binding (GO:0008270). The SLY protein contains a COR1 chromatin-binding domain, and it was suggested in H. sapiens. To demonstrate its robustness, we further analyzed the method for its performance on D. melanogaster. For this study, we integrated protein interaction data from S. cerevisiae, C. elegans, and H. sapiens to form the reference dataset to determine functional annotations of proteins in D. melanogaster, the target dataset. None of the protein pairs in D. melanogaster were involved in training our model. In other words, the interacting domain patterns were extracted purely based on interaction pairs from S. cerevisiae, C. elegans, and H. sapiens. Function annotations were effectively assigned for 447 D. melanogaster proteins. Among the 447 proteins, CSIDOP accurately assigned function annotations to 419 proteins (i.e. 93.73% in accuracy).The CSIDOP is shown above to produce highly accurate function predictions for proteins in D. melanogaster protein CG15912 [Swiss-Prot: Q9W4J7] was detected by CSIDOP to exhibit ATPase activity, coupled to transmembrane movement of ions, phosphorylative mechanism (GO:0015662). Its orthologs: Haloacid dehalogenase-like hydrolase domain containing 3 [Swiss-Prot: Q9BSH5] in H. sapiens and [Swiss-Prot: Q9CYW4] in M. musculus were both found to be associated with phosphoglycolate phosphatase activity (GO:0008967) and hydrolase activity (GO:0016787), which is an ancestor term of our predicted term (GO:0015662). Moreover, for the protein CG18445 [Swiss-Prot: Q9V5F2], a multispan transmembrane protein related to fly Porcupine, our algorithm identified to carry out the O-acyltransferase activity (GO:0008374). Through literature search, we discovered that biological experiments conducted by Kraut et al. In addition, we were able to discover novel annotations for some proteins. For example, the 1F–2F given interacting domain pattern 1D–2D (Eq. 1), where ‘-’ denotes interaction. In other words, 1F and 2F represent function assignments to proteins in the query interaction pair with modular domains 1D and 2D, respectively.1F–2F, 1D–2D) is calculated by counting the number of interaction pairs in the reference dataset that contain the interacting domain pattern 1D–2D and have the corresponding functional annotation of 1F–2F, and P(1D–2D) is computed by counting the number of pairs that contain the interacting domain pattern 1D–2D. For a query protein interaction pair, the posterior probabilities of all possible function pairs are calculated, and finally, the top ranking function pairs are assigned. In this step, we were able to predict function assignments for 1546 human proteins, but with lower accuracy of 90%.Since the CSIDOP method only keeps the most significant interacting domain patterns from the closely related protein interaction pairs across species, PPI pairs in the test dataset not containing the patterns in the lookup table will result in no prediction. To enlarge the coverage, we can use a two-step prediction method: the first step will predict functions for a large number of proteins with lower confidence, and the second step uses CSIDOP for more accurate prediction. In the first step, for each protein pair in the test dataset, we construct a list of all interacting domain patterns. Then for each of these plausible domain patterns, we try to collect a list of protein interaction pairs in the reference dataset that contain the pattern. Numerous interaction pairs with shared pattern may exist in the reference dataset, and certain functions annotated to those pairs may be more likely to be associated with the target protein pair than other functions. Thus, in order to assess the probability of each functional assignment, we calculate the conditional probability of a protein interaction pair having function pair p having term t, terms with probabilities above certain threshold can be treated as positive prediction and terms below the specified threshold can be treated as the negative prediction; thus, sensitivity and specificity measures can be calculated. Applying the same criteria in Nariai et al. p-t association is obtained from the GO. The observed negative association set is defined as follows: if the association is not found in the positive set and term t is neither ancestor nor descendant of the known function terms in GO hierarchy for protein pp-t associations in our positive prediction list, but are observed to be in the negative set by GO. Lastly, false negatives are the p-t associations in our negative prediction list, but should be in the positive list. For varying posterior probability cutoffs, the relationship between sensitivity and 1-specificity is plotted in a ROC curve  2 statistics to derive meaningful interacting domain patterns from these PPI groups. In order to extract the true functional interacting domain patterns from the vast wealth of deposited protein-protein interaction (PPI) data, we have devised an algorithm to find groups of protein interaction pairs with similar functions and applied χt. For instance, assume that there are two PPI pairs A–B and C–D, where ‘-’ denotes interaction. The two pairs are grouped together if and only if the following condition is satisfied; the closest GO-graph-node distance between either  and  or  and  are less than or equal to the threshold. In the end, PPI pairs in the same group are assuredly more likely to share the same or similar functions. In our application, the value of t is empirically set to be two.In the training phase, for each PPI pair in the reference dataset, we tried to determine its close neighbors or functional similar interaction pairs. More precisely, each PPI pair in the training dataset serves as a centroid to form a group of protein pairs with similar functions. In doing so, all remaining pairs are compared against this centroid interaction pair. An incoming PPI pair is accepted to join the group if and only if the distances among individual proteins in the centroid pair and the pair under consideration are below certain threshold After constructing a group of functional similar PPI pairs, we derive the most representative interacting domain patterns from each. This is accomplished by identifying an interaction domain pattern that is uniquely conserved in a group of PPI pairs across different organisms and with the same or similar functions (i.e. in the same group). Proteins often contain multiple domains, and one or more domains may form a functional unit during interaction, which we call a modular domain. Thus different combinations of modular domains in a protein should be considered in generating the potential interacting domain patterns. Due to the existence of some big proteins with more than 15 domains, it is computationally intensive and impractical to generate all possible combinations; therefore, measures had to be taken to trim down the set of all possible combinations by restricting the domain size of each protein to 4. While domain combinations involving more domains from each protein could slightly increase the prediction accuracy, they require much longer computational time. The assumption is also biologically reasonable because it is unlikely for a large number of domains to come together and form a single unit during interaction. Moreover, the same set of a large number of domains is unlikely to occur repeatedly in other proteins.2 statistics is calculated for each pattern. The χ2 value is computed using the following formula,N is the total number of PPI pairs in the reference dataset. Variable A is the number of PPI pairs in the group that contain the particular ‘pattern’, and B is the number of remaining PPI pairs outside the group that contain the ‘pattern’. Variables C and D are the number of PPI pairs that do not contain the ‘pattern’ in the group and in the remaining samples outside the group, respectively. An interacting domain pattern occurring more frequently in PPI pairs inside the group than outside the group is expected to have a higher χ2 value, hence is more significant. Finally, the deduced interacting domain patterns with the highest χ2 value are adopted in a lookup table for function annotation.In consequence, a list of potential interacting domain patterns is enumerated from each protein pairs in an individual group of PPIs with similar functions. Each domain pattern will be associated with a list of function terms from their corresponding PPI pairs. In order to select the most significant interacting domain patterns, χText S1A list of domains and their corresponding IDs(0.29 MB TXT)Click here for additional data file.Text S2A lookup table of domain patterns and associated functional assignments(0.62 MB TXT)Click here for additional data file.Text S3H. sapiens in text formatA complete list of novel functional predictions for proteins in (0.01 MB TXT)Click here for additional data file.Table S1H.sapiens proteins found with supporting evidences. For each human protein in the 1st column, highlighted terms in the 2nd column are the GO terms that CSIDOP predicted and also supported by evidence found in other databases or literature. The evidence is shown in the 3rd column where it lists the orthologous or paralogous proteins annotated with these highlighted terms inferred using different techniques. For example, we predicted the protein Q96A23 to have the function GO:0001786, and we found that its paralog Q99829 protein in H. sapiens is detected with GO:0001786 through the evidence code IDA. InterPro is a database of protein families, domains and functional sites in which identifiable features found in known proteins can be applied to unknown protein sequences. IntAct is by Giot et al. The following is a list of evidence codes used in the table.Novel functional annotations for some (0.04 MB DOC)Click here for additional data file.


The Human Immunodeficiency Virus type one (HIV-1) is the major causing pathogen of the Acquired Immune Deficiency Syndrome (AIDS). A large number of HIV-1-related studies are based on three non-human model animals: chimpanzee, rhesus macaque, and mouse. However, the differences in host-HIV-1 interactions between human and these model organisms have remained unexplored.Comparative Analysis of Protein Interactions for HIV-1), the first web-based interface to provide comparative information between human and the three model organisms in the context of host-HIV-1 protein interactions. CAPIH identifies genetic changes that occur in HIV-1-interacting host proteins. In a total of 1,370 orthologous protein sets, CAPIH identifies ~86,000 amino acid substitutions, ~21,000 insertions/deletions, and ~33,000 potential post-translational modifications that occur only in one of the four compared species. CAPIH also provides an interactive interface to display the host-HIV-1 protein interaction networks, the presence/absence of orthologous proteins in the model organisms in the networks, the genetic changes that occur in the protein nodes, and the functional domains and potential protein interaction hot sites that may be affected by the genetic changes. The CAPIH interface is freely accessible at http://bioinfo-dbb.nhri.org.tw/capih.Here we present CAPIH (CAPIH exemplifies that large divergences exist in disease-associated proteins between human and the model animals. Since all of the newly developed medications must be tested in model animals before entering clinical trials, it is advisable that comparative analyses be performed to ensure proper translations of animal-based studies. In the case of AIDS, the host-HIV-1 protein interactions apparently have differed to a great extent among the compared species. An integrated protein network comparison among the four species will probably shed new lights on AIDS studies. Cross-species virus infections usually raise serious threats to worldwide public health. Examples include the Acquired Immunodeficiency Syndrome (AIDS) and Avian Influenza, where viruses cross species boundaries to infect humans from simians , and birHomo-Pan divergences in protein-protein interactions (PPIs) have remained elusive. Nevertheless, it is feasible to pinpoint species-specific post-translational modifications (PTMs), which are known to affect host-virus protein interactions [As one of the most deadly diseases to humans, AIDS is barely life-threatening to chimpanzees , human'sractions ,13 and cractions . So far,Comparative Analysis of Protein Interactions for HIV-1) – for comparative studies of genetic differences between the human proteins involved in host-HIV protein interactions and their orthologues retrieved from three mammalian species: chimpanzee (Pan troglodyte), rhesus macaque (Macaca mulatta), and mouse (Mus musculus). The three latter species are all important animal models for HIV studies [Here we develop a web-based interactive database – CAPIH  physical interaction; (2) regulatory interaction; (3) post-translational modification; (4) transportation; and (5) positive interaction (6) negative interaction (7) others. The classification of interaction key phrases can be found online at the BIND  and HPRDthe BIND .The numbers of species-specific genetic changes identified by CAPIH are shown in Tables The CAPIH interface provides five query schemes: by gene accession number, gene description, gene ontology, protein domain, and expressing tissue Figure . Alternahttp://www.jalview.org.The interface also provides an alignment viewer using JalView  The "Mu. JalViewhttp://bioinfo-dbb.nhri.org.tw/capih/help.php?search_target=help. The identified species-genetic changes are downloadable at http://bioinfo-dbb.nhri.org.tw/capih/download_table.php?search_target=download.CAPIH also provides a JAVA-based adjustable protein interaction viewer  with the HIV-1 Vpr accessory protein is known to be critical for HIV-1 infection (. InspectHere we present the first web-based interactive tool for comparative studies of host-HIV interactions in four different model animals. The interface may provide new insights into HIV studies. Firstly, although mouse is an excellent model for HIV studies, considering the large genetic divergences that occur in protein domains between human and mouse as shown here, many of the host-HIV protein interactions are expected to differ between the two species. Therefore, the differences in genetic backgrounds must be controlled for appropriate interpretations of mouse-based HIV studies.Secondly, human viral infections transmitted from other species have become a critical issue because humans usually lack the immunological arsenal to fight such viruses ,40-42. CThirdly, one surprising finding here is that PTMs actually differ to a great extent among the four compared species, considering that they are genetically close to one another. For example, human and chimpanzee differ from each other by an average of two amino acids per protein . In compThe CAPIH interface is unique because it is the first web-based tool to provide comparative information of genetic changes and PTMs in host-pathogen interactions. Since cross-species viral infections have become a critical issue in public health, comparative studies of host-pathogen interactions deserve wide attention. Specifically, comparative analyses of host-HIV interactions may shed some light on the mechanisms of differences in AIDS progression between human and chimpanzee. A number of possible mechanisms have been proposed ,45. Howehttp://bioinfo-dbb.nhri.org.tw/hivppi/. The JAVA Runtime Environment is required to view the interactive protein networks.The CAPIH database is available at AIDS: acquired immunodeficiency syndrome; CDS: coding sequence; HIV: human immunodeficiency virus; indel: insertions/deletions; PTM: post-translational modification; PPI: protein-protein interaction; UTR: untranslated region.FCC conceived the project. FKL, CLP, and JMY analyzed the data. FKL and CLP constructed the interface. FCC, FKL and TJC drafted the manuscript. All authors read and approved the manuscript.


Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant.Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage.We first predicted the protein protein interaction in Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.Our interactome is a comprehensive genome-wide network in the organism plant  Homo Sapiens[Drosophila Melanogaster[Saccharomyces Cerevisiae,[Caenorhabditis elegans[A protein in an organism does not fulfill its function independently. The complicated cellular functions of an organism frequently rely on physical interactions between proteins. Over the past decade, various new experimental methods have been developed to quantify the interaction between proteins. Some of them, like AFM, FRET and BREF, can only be applied on the small scale protein-protein interaction detection, while others, such as Yeast Two Hybrid, Affinity Purification and Protein Arrays, are suitable for high throughput purpose. High throughput technologies, like yeast two-hybrid system (Y2H) and affinity purification, have been applied for genome-wide detection of protein protein intearction in several species, including o Sapiens-4, Drosoanogaster,6, Sacchrevisiae,-9and Caes elegans.Homo Sapien, Wanker and his colleagues have constructed the protein-protein interaction network with 3186 protein pairs containing 1705 proteins, while Vidal et. al. have built a protein-protein interaction network with the same platform. However, only a few small overlapping pairs are detected between the two protein interaction datasets. Same phenomenon also is observed during the process of building protein protein interaction network in Drosophila Melanogaster. Giot et al have built an interactome of Drosophila Melanogaster containing 4,679 interactions between 4,780 proteins. Later on, another group reported a protein interaction map with 2338 interactions between 1727 proteins using a high-throughput yeast two hybrid system. Surprisingly, only 24 interactive pairs are overlapped between the two datasets. The same results emerge in Saccharomyces Cerevisiae also. Among three different PPI datasets generated by three independent research groups with 4,475, 72,690, and 48,751 PPI pairs, respectively, maximum overlaps are below 9% between each two interactomes. The first two interactomes are generated with the same technology - yeast two hybrid system, and the last one is done with Affinity Purification-MS method. These previous independent results show a low consistence between different research groups. Analyses of the differences in the datasets and methods above indicate that the high throughput experimental methods have condition-specific and method-specific characteristics, which result in the little overlap even with the same experiment platform in the same species. Thus each of the high throughput experiment methods has its bias in detecting protein-protein interaction and alternative strategies should be adopted to enhance the accuracy and comprehensiveness of the genome-wide protein interaction maps. However, the high-cost, low coverage, and labor intensive of those experimental methods limit their applications.In Homo Sapiens, Saccharomyces Cerevisiae, Drosophila Melanogaster, Caenorhabditis Elegans, Plasmodium Falciparum and Arabidopsis Thaliana, have been already built on the basis of certain bioinformatics methods. These available interactomes have been constructed by combining different high-throughput data with bioinformatics predicted results. For example, the integrated protein-protein interaction network of Saccharomyces Cerevisiae was derived through the integration of the Y2H data, protein complex and microarray gene expression profiles with the Bayesian probability model [Arabidopsis thaliana were constructed based on the data of ortholog and collected transcriptome data, or shared annotation, domain interaction, and co-localization [Plasmodium falciparum protein interactome [In order to overcome the drawbacks and the limitation of the experimental approaches and expand the coverage of protein protein interaction network, various bioinformatics approaches have been developed during past decade. For example, new bioinformatics methods, based on the gene expression profile, the evolutionary relationship and genome contexts, are introduced in the field of genome-wide protein-protein interaction network construction, those methods include ortholog,11-13, mty model . The proty model . The twolization . The Plaeractome  was estaeractome . To systeractome ,15,19.Arabidopsis thaliana on the basis of model organism protein-protein interactions, protein domain assignments, Arabidopsis thaliana gene expression profiles, biological function annotations, and genomic inference methods. During the process, we have used carefully constructed high quality Golden Standard Positive (GSP) data and Golden Standard Negative (GSN) data to enhance the reliability of Bayesian probabilistic model and generated the protein-protein interaction network with a larger coverage. Next, we have carried out functional analysis on the basis of our Arabidopsis thaliana PPI network. We have also annotated protein function by using the simulated annealing method. In addition, we have deposited all our results and other integrated useful resources into AtPID [By assembling a collection of genomic, transcriptomic and proteomic data, we have successfully predicted genome-wide protein-protein interactions in to AtPID . The resArabidopsis Thaliana. After the construction of the network, we validated our network by using data from KEGG[During the process, we first defined a gold standard positive set (GSP) through text mining from published literature as well as data collection from IntAct, BIND and TAIR database. We generated a gold standard negative set (GSN) on the basis of the different sublocation information from GO. Data integration was mainly fulfilled by measuring individual accuracy in the context of overlap with the gold standards, and subsequently assigning them weights or scores according to the number of overlaps. The scores calculated here were then used to construct the main protein-protein interaction network of from KEGG,22, AraCfrom KEGG,24 and pA gold standard positive (GSP) set of 4,129 distinct protein-protein interactions among 5,505 proteins came from the databases IntAct , BIND2626,27, TAWe applied ortholog interactomes, expression data, shared biological function, enriched domain pairs, gene neighbor, gene fusion and phylogenetic profile combined with prior odds to predict protein-protein interactions. By applying Naïve Bayesian approach ,31, we cSince shared biological function represents the consistence of protein function more than real physical interactions and genome context methods  imply functional linkage , the respost=1, to define the cutoff of high confident interactive pairs. Under this condition, the LR is calculated to be 559. There are 2505 interactions concerning 1361proteins in our high confident network. Secondly, As the interaction predictions based on ortholog are more reliable, among the LR generated from 4 different ortholog interactomes, Caenorhabditis elegance has the lowest LR with the score 92, we used 92 as the cutoff for the medium confident interactions in our network, the 16429 interactive pairs with 5363 proteins. Last, the interaction pairs having the LR score lower than 92 but being supported by at least one predictive method are collected in the low confident network. Thus, we use 92 and 559 as the likelihood ratio cutoff and separate our PPI network in low, middle and high confident groups.After the construction of the network, we categorized PPI pairs into three groups according to LR score. Firstly, we applied the generally accepted Bayesian criterion, OThe simulated annealing algorithm was usedE.coliK12, S.pombe, and S. cerevisae inferred from the Arabidopsis proteins in our high and medium confident networks are extracted from the Inparanoid database. Next, corresponding protein-protein interaction pairs from the individual organism were built in accordance with the protein-protein interaction in our interactome. Since the ortholog information in S. cerevisae was used to construct our PPI network, we first excluded the predicted PPI in A. thaliana inferred from the S. cerevisae orthologs in our network; we next reversely mapped the rest protein interaction pairs to S. cerevisae. Then, we search all of the new built pairs in IntAct databases. In this way, we obtained fifty conservative pairs in S. cerevisae. The result indicates that many of our predictive protein-protein interaction pairs are conserved in other organisms. For instance, our predictive interaction pair, AT2G33210 and AT2G39770, has corresponding interaction pair, NP_418567.1 and NP_416543.1, in E.coliK12, which has been confirmed by the pull down experiment [S. cerevisae, the protein YLL036C represents the splicing factor associated with the spliceosome and the protein YDL030W is a subunit of the SF3a splicing factor complex. The interaction pair confirms the interaction between splicing factor and the related spliceosome complex [In order to verify the interactions in our network, the orthologs in  complex ,35. All Arabidopsis thaliana from KEGG and AraCyc to check the consistence between pathway and our network for the validation of our interactome. There are totally 299 pathways in KEGG and AraCyc. Firstly, we counted the number of the pathways that a protein participates in. Among all the member proteins of these pathways there were 33 proteins that join in more than 10 pathways  from KEGG and used their neighbors in our high and medium confident network to build an ath00010 related PPI network. Figure S. pombe, which we did not applied in our predictive process, we found that the related ortholog protein in both the organisms also interacted with malate dehydrogenase and other proteins concerning the function of energy metabolism. These functional consistencies related to mitochondrion and interactive conservation in energy metabolism across species could validate the value of our network in studying metabolic pathways or related function modules. Moreover, we have tried to extend the pathway with the help of our interactome. For example, the protein AT1G48790, which is annotated as ubiquitin-dependent protein, is not the component in the aerobic respiration alternative oxidase pathway. But in our network, AT1G48790 has interaction with AT1G16700 which is one of the components of the pathway represents NADH dehydrogenase (ubiquinone). In addition, AT1G48790 involves in the pathways such as NADH phosphorylation and dephosphorylation, oxidative phosphorylation, and aerobic respiration. it makes us suppose that the aerobic respiration alternative oxidase pathway might interact with other biological components or above pathways by means of the interaction between AT1G48790 and AT1G16700.Moreover, we have mapped all the pathways to our high and medium confident network. Almost all the metabolic pathways (299 pathways) have their key components in our network  are important to understand the mechanisms of complex transcriptional regulations in organisms. We used our protein-protein interaction data to infer the possible interaction between transcription factors. For example, we found interaction between AT1G56650, that involves in abscisic acid (ABA) signal transduction, and AT2G40220, that response to abscisic acid stimulus. Both of the TFs could be the downstream components of the ABA signaling pathway that participate into the regulation of target genes response to ABA signal. Therefore, these interactions between transcription factors may exposit certain regulatory mechanism of the transcription factors in the biological processes. Researchers could refer to AtPID to check the interaction partner of their interested transcription factors.Arabidopsis thaliana Protein Interactome Database(AtPID)[http://www.megabionet.org/atpid/. One can explore our interactome with the help of AtPID.All our predicted PPI pair and the whole network is stored in se(AtPID), which iArabidopsis thaliana are publically available. One of the interactomes is mainly based on the method of ortholog data combined with gene co-expression relationship, that contains 7,072 high and medium confidence protein-protein interaction pairs between 1,650 proteins [Arabidopsis thaliana. Therefore, we constructed our interactome in a more comprehensive way by integrating the information concerning ortholog, coexpression, genome context, functional annotation, and protein domains with Bayes probabilistic approach. The number of the overlapped interactive pairs is 3,054, involving 1,444 proteins, between our high and medium confident interactome and Lee’s interactome. In contrast, our high and medium confident interactome only contained the same 791 consistent interactions between 635 proteins as Chen’s interactome. Similarly, the overlap between the previous two networks is just 338 interactions concerning 335 proteins. The differences between the datasets could result from the criterion used by different groups. Our domain based interactions came from two different approaches, one part of the interactions were constructed according to the directly domain interaction that were experiment validated, meanwhile, another part of the interactions were inferred from domain interaction based on the experimental confirmed protein interaction data. In contrast, the domain features in Chen’s methods were based on seven predictive methods come from Pfam system, thus the domain interaction criterion adapted in our methods is more stringent. Moreover, Lee’s and our group has adopted similar methods such as ortholog etc, that could contribute to higher overlapped interactive pairs between the two resulted interactomes. However, Lee’s group just uses ortholog to construct the interactome and the coverage of their interactome is much low than ours. Besides, during their analysis process, they simply evaluate the goodness and stability of a protein-protein interaction with a CV value without applying any powerful statistical integrative method. The differences among the three interactomes denote that each of the three datasets could be complementary to the others. In addition, more various methods could be applied in the field of protein protein interaction predictions to generate more comprehensive interactiome.Because of the importance of protein-protein interaction, an interactome will be an invaluable resource for studying the protein functions, sublocations and protein protein interactions, and inferring the potential mechanisms of biology processes. Currently, two predictive interactomes of proteins , while aproteins . Since eproteins , the resA in ATP represents adenine that is a purine base. We performed similar analyses on the rest 47 GO terms, 26 of them showed direct biological relations  was constructed on the basis of the data from databases IntAct, BIND, TAIR and text mining. During the process of text mining, we have collected PPI data with experimental evidence and references. To ensure the reliability of these data, we also conducted a validation process. First, PPIs collected from the literature without AGI locus identifiers were mapped to IPI. Symbols without a match were removed. 3,866 protein-protein interaction pairs involving 1,875 proteins were extracted based on this filtration process. Additionally, protein components in partial enzyme complexes were also added to GSP based on the assumption that components of an enzyme complex have not only high functional association but also potential physical interactions. Enzyme complexes from KEGG were dowSacchromyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and homo sapiens[Arabidopsis thaliana with the help of Inparanoid [Arabidopsis thaliana derived from the interactomes in the above four species is showed in table In terms of the method of ortholog interactome, we followed the methods proposed by Chinnaiyan . Firstlyo sapiens-42Secondparanoid -45. ThenGene expression profile is also generally used to infer protein protein interaction since previous studies have already demonstrate that the genes encoding interactive proteins have the similar expression patterns under different conditions. We have chosen five groups of chips: ME00345, ME00338, ME00331, ME00326 and ME00319 to detect the similarity of the gene expression patterns. After preprocessing of these chips with MAS5 we calculated the Pearson Correlation for each pair of genes among the above chips with R package. The ideDomain is a function unit of a protein and participates in intermolecular interactions, each protein is a collection of conserved domains. Therefore, it can infer the protein interaction from related domain interaction, we could conclude that two proteins have interactions if domains in each protein have confirmed interactions. We got domain interactive information from Pfam database and induced the PPI information through mapping domain interactive data to protein information from TAIR (7). /P(neg)OThe posterior odds or the odds that two proteins have interaction based on new predictive evidence are defined as:Where Likelihood score, which reflect both the sensitivity and specificity, for each corresponding set of prior odds were computed based on a derivation of Bayesian rule:With the approach described above we could get the LR score of each PPI pair, and the main LR of a certain PPI pair is the product of the LR score from different methods.The Naïve Bayesian method is used to integrate the datasets that are independent to each other. However, the independence of our methods of predicting PPI is hard to affirm and we are unable to prove that the features used in our study to be independent. We have explored the independence of these methods and calculated the combined LR of each two methods, but the criterion of independence between two methods is hard to be fixed (data not shown). Given that gene neighbor, gene fusion and phylogenetic profile are all predictive methods on the basis of the sequence information, their independence can therefore be regarded weakly. Furthermore, SSBP represents the functional consistence of two proteins more than real physical interaction and two proteins that have domain interactions may not have physical interactions because of protein location or other biological constrains. In order to follow the requirement of Bayesian approach and enhance the reliability of our predictive interactome, we combined the methods of gene neighbor, gene fusion, phylogenetic profile and SSBP together and validate the PPI pair obtained by these four methods with protein domain method. The result of this combined new PPI predictive method is treated as representing the aspect of functional consistent of a PPI pair, which is a new feature used in our Bayesian Integration process. As to the methods of ortholog and gene coexpression, their hypotheses based on which that a PPI pair has been predicted to have interaction are different and do not relate to the functional consistent. We have regarded that these three methods, including ortholog interactome, gene expression and the new combined method are independent to each other. The final LR score of our PPI network is the product of the LR scores of the three above methods.The authors do not consult to the supporter of this study. The authors declare that they have no competing interests.TS, YL designed the study and GL implemented the algorithms and data integration. GL and FX analyzed the results, FX and TS drafted the manuscript, FX, CZ, PL, JC and YD collected the data and participated in the analysis and discussion. All authors read and approved the final manuscript.The annotation result of unknown proteins with annealing simulation algorithm on the basis of our interactome.Click here for fileThe conservative PPI are listed. Moreover, further information about the PPI pairs such as their ID, GO annotation, species , the experiment that validate the PPI, the related literature etc. are listed under the conservative PPI pairs.Click here for fileThe pathway related interactions are stored in additional file Click here for fileThe first line of each part is the GO term that enriched in the tissue specific protein list. The information below the first line is the GO terms that enriched in the interactions partner of the first line listed GO term. All of the GO enrichment analysis related information including GO-ID, p-value, corr, total Description Genes in test set are listed.Click here for fileThis file contains the result of new published validated protein interaction pairs in our predictive interactome.Click here for fileThe tables in the file contains the information about the predictive result of the seven methods applied in our study.Click here for file


Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction.To assess the ability of the proposed method to recognize the difference between "Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI. Protein-protein interaction is intrinsic to most cellular processes and can aid significantly in identifying the function of newly discovered proteins and in understanding the molecular networks they participate in . TherefoThe technical limitations associated with the mentioned biochemical approaches have resulted in a growing need for development of computational tools that are capable of identifying PPIs . Hence, et al. ◦ E.g. The algorithm for feature extraction is illustrated in Figure SW between protein sequences s0 and the subsequence s1 is the score of the best local alignment with gaps between the two protein sequences, computed by the SW dynamic programming algorithm [The Smith-Waterman score lgorithm .et al. [π a possible local alignment between protein sequences s0 and the subsequence s1, defined by a number n of aligned residues, and by the indices 1 ≤ i1 < ... <in ≤ |s0| and 1 ≤ j1 < ... <jn ≤ |s1| of the aligned residues in s0 and s1 respectively. Let us also denote by ∏ the set of all possible local alignments between s0 and s1, and by p the score of the local alignment π ∈ ∏ between s0 and s1, the Smith-Waterman score SW between s0 and s1 can be written as Following Saigo et al. , let us The process is illustrated in the following example:s0, s1, s2, and s3 in the database is S* = {admn,qghk,il,ged}. The number of elements in S* is m = 4. We also assume that we have prior knowledge about the interaction information between these proteins:Let us assume that the set of enumerated protein sequences n = 4. We then concatenate all sequences in S* to obtain s' = admngqhkilg ed with length l = 13. Next, we compute the pairwise score between each protein sequence in S* and the substrings created by shifting a window of a size n along S. It's common in bioinformatics to slide a window by a single position however this will generate more subsequences than simply shifting the window by its size. For instance, sliding a window of size 4 over s' yields 10 subsequences, however shifting it by its size yields only 3 subsequences. The two notions have been tested and the results suggested no significant differences in accuracy (results not shown). Using a shifting window over the concatenated sequences of the training set may lead to generating a subsequence comprises of the end of one sequence and the beginning of the next sequence. This, however, is not a problem since all protein sequence of interest score against the same subsequence.We choose a window size m × k. In this case k = 3, because the size of the last window shift of s' is 1, i.e. score cannot be computed for a sequence of size less than 2 amino acids. This implies that, the resulting matrix will have a size of 4 × 3. After that, the score vectors of the "interacting" proteins are merged and added to the set S1, and the ones of the "non-interacting" proteins are added to the set S2.The results are then stored in a matrix of size We believe that the feature extraction is particularly significant step in our method to predict PPI. More meaningful features yield better generalization performance . The feainteracted" or "non-interacted". This representation is combined with SVM to classify between the two sets. The SVM algorithm addresses the general problem of learning to discriminate between positive and negative examples of a given class of n-dimensional vectors. In order to discriminate between "interacted" proteins (positive examples) and "non-interacted" proteins (negative examples), the SVM learns a classification function from a set of positive examples χ+ and set of negative examples χ-. The classification function takes the form:The problem is basically formulated as a two-class classification problem: both training and testing sets contain protein pairs belong to either "λi are computed during training by maximizing a quadratic objective function and the function K is called a kernel function [x is then predicted to be positive if the function f(x) is positive. More details about how the weights λi are computed and the theory of SVM can be found in [where the non-negative weights function . Any newfound in -33..The datasets can be downloaded from NZ has contributed to the conceptual development of PPI-PS, designed and implemented the method, performed the experimental work and the statistical analysis, drafted the manuscript. SL formulated the problem solving algorithm. WE contributed to the implementation of PPI-PS. PC contributed to the manuscript writing. All authors read and approved the final manuscript.