Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages. Proteins are involved in almost all physiological aspects of cellular life from the catalysis of biochemical reactions within the intermediary metabolismn to the processing and integration of internal and external signals. The misregulation of protein expression results in pathological states such as cancer, neurodegenerative diseases and metabolic imbalances. Proteins are synthesized by translating the information encoded in a RNA molecule to a polypeptide chain, which adopts a specific three dimensional structure. Proteins are subjected to a constant turnover making protein homeostasis a very important feature of their regulation. Many proteins function within large multimeric complexes that are highly dosage dependent. The recent developments in gathering large scale genomic, transcriptomic and proteomic data pose substantial challenges to the bioinformatic processing of these data, which have yet not been completely solved. In case of the proteomic investigation, the challenges occur at all levels ranging from sample preparation and data gathering over the raw data integration and database searching to the functional interpretation of large datasets. Based on a iterative strategy of proteomic analysis, data interpretation and sytstematic challenges, hypothesis can be developed and modified, which will eventually lead to the generation of new knowledge Figure .All proteins from a sample of interest are usually extracted and digested with one or several proteases  to gene2) provide information about the sequence of the peptide, which is key to their identification. For a shotgun approach, no prior knowledge of the peptides present in the sample is required to define peptide selection criteria during the LC-MS analysis. Therefore, the peptides eluting from the chromatographic column are identified in a data-dependent mode [The peptides obtained are subsequently analysed by liquid chromatography coupled to mass spectrometry (LC-MS). The two most common approaches here are either designed to achieve a deep coverage of the proteome (shotgun MS ) or to cent mode , where cent mode , less ab2) for those peptides that were selected during any of the data dependent cycles. The intensity of a certain peptide m/z can be plotted along the RT to obtain the corresponding chromatographic peak. The area under this curve (AUC) can be employed to quantify the corresponding peptide. On the other hand, the peptide identification is achieved through its fragmentation spectrum.The data can be displayed as a 3-D map with the mass-to-charge ratios (m/z), retention times (RT) and intensities for the observed peptides as axis, together with fragmentation spectra . The databases are normally protein databases translated from genomic data [The large number of MShms . Introdogy.org, . Therebyogy.org, . For sinogy.org, . For larogy.org, ,35 have ogy.org, .http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools). For instance, the DAVID and Babelomics software resources are often mentioned when it is necessary to analyze large gene list but currently there are more than 60 tools calculating GO term enrichment [The first step after GO-term annotation is a GO-term enrichment analysis to compare the abundance of specific GO-terms in the dataset with the natural abundance in the organism or a reference dataset, e.g. different cell lines, inhibitor treatment or growth states . To extrrichment -40. Mostrichment . GOStat,richment . Those prichment . Weinertrichment . In theirichment . Despiterichment .pathguide website http://pathguide.org[A pathway describe the series of chemical reactions in the cell that lead to an observable biological effect. Proteins involved in the chemical reaction and those that have regulatory influence are combined in so-called pathway databases. Similarly to the previously described GO term enrichment analysis, protein or gene lists can also be scrutinized for pathway abundances which might be more meaningful because it moves the data interpretation away from the gene-centric view towards the identification of functional biological processes. Furthermore, functionally independent proteins can share some GO term associations, for instance for very general terms such as "binding" or "cytoplasmic". A high number of resources and databases is available to extract pathway constraints from biological data Figure . Comprehguide.org. Nowadayguide.org. The tesThe majority of proteins do not act as independent entities. They form either transient or stable complexes with other proteins that act as scaffolds or regulate the protein activity. A protein can be involved in mulitiple complexes of varying composition and to completely understand a biological system it is necessary to analyze the abundant protein complexes as well as the conditions that lead to their formation or dissociation. Information on protein interactions in complexes is deposited in interaction databases such as MINT, BioGRID, IntAct or HRPD -57, assoProtein interactions are often displayed as large interaction networks illustrating the high degree of connectivity nand the presence of promiscous hub proteins. A widely used resource for interaction data is STRING, which is not only a database itself, but connects to several other data resources to and is therefore also capable of literature mining ,62. FurtWhen working in not yet or just recently-sequenced organisms, data bases might not contain the complete set of protein descriptions. Similarly, proteins of unknown function might also be identified from highly curated databases of well studied organisms. Those proteins often lack the previously described information on interactions and pathway affiliations so that they would not be found in such studies. To learn more about the function of those proteins and how they interact with members of certain pathways, it is helpful to analyze their amino acid sequence for specific folds of protein domains or for motifs for post-translational modifications. The simplest analysis represents a BLAST search against the database of known protein sequences to find if proteins with similar amino acid sequences have been described in other organisms . FurtherThe development of methods to systematically study all proteins in a cell and their subsequent functional annotation opens up new pathways of research. In the future it is very likely that such studies will uncover new principles of how biological systems operate hopefully leading to an improved treatment of human pathologies.Over the last ten years the analytical harware has reached a level of sophistication of a more mature scientifc field. However, the bioinformatic interpretation and the processing of the data are still in its infancy. Besides reliable and robust algorithms, international standards for data processing and deposition as well as their interpretation have to be developed and agreed upon in order to unleash the full potential of proteomic research.AS, IF and AI wrote subsections of the paper and were involved in assembling the manuscriptThe authors declare that they have no competing interests.


AFAL allows the user to define a wide variety of filtering criteria  in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand–protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5′-triphosphate.With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein–ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: The online version of this article (doi:10.1007/s10822-014-9783-6) contains supplementary material, which is available to authorized users. These ligands include a large number of small organic biomolecules, such as adenosine-5′-triphosphate (ATP) or heme as well as inorganic ions and molecules, for example transition metal ions including Fe or Zn2+ . Such liThe study of the specific interaction of a protein with its ligand is an active research field because of the implications this has in the overall understanding of the structure and function of proteins, and in particular in the fast-growing area of structure based drug design . A numbeEscherichia coli (17.4 %), Thermus thermophilus (18.3 %), Haloarcula marismortui (10.9 %), Saccharomyces cerevisiae (8.4 %), Bos taurus (9.7 %), Homo sapiens (9.1 %), and others [In today’s research environment, a wealth of experimental and theoretical structural data is available. There are currently 96,980 macromolecular structures stored in the RSCB Protein Data Bank , 70,908 of which correspond to proteins that contain ligands  as part of their structures and which belong to diverse organisms including ismortui .9 %, Sacd others .+) to complex pyrrolic rings (e.g. heme) and non-standard polymers [Ligands in the PDB currently encompass 16,447 different chemical components, ranging from single atoms  application analyzes the protein structures stored in PDB and identifies the amino acids and atoms involved in the interaction with any ligand . AFAL displays the protein–ligand interaction atomic distances and calculates the frequency of the amino acids that surround a particular ligand and the frequency of the atomic interactions per residue. Identification of the most likely pattern of residues implicated in the binding of given ligand, independently of fold and phylogenetic background, can be useful not only to derive insights into the nature and evolution of specific protein–ligand interactions and the understanding of molecular and atomic level interaction mechanisms but also in applied studies related to drug design or modification of functional groups in proteins of biotechnological interest.AFAL has been compiled using pre-existing and publically available resources and software packages Fig.  such as A local database was created to facilitate quick access to the structural data stored in PDB and to adequately classify the information to be retrieved in each search according to user selected filters. The database was built using a MySQL engine version 14.14. To populate the database and to classify the PDB files, multiple scripts programmed in Perl language were generated. More than 90,000 files from PDB were accordingly classified into proteins with a ligand, proteins without ligand, type of ligand, protein family, organism and crystal resolution. The local database also stores the results of each new query consulted. This facilitates the access for new users to pre-calculated amino acid frequencies around commonly consulted ligands. The AFAL database is automatically updated every month to include actualized PDB files in every new search.Homo sapiens). Both filters have customized menus, using a list of pre-defined protein families or organisms. If the protein family or source organism is not in the list, the user can type in the respective name(s) using the IUBMB Enzyme nomenclature database link and/or the NCBI taxonomy database link. Since many of the structures in the PDB database are highly similar or even identical, a further filter avoids biasing of the results towards multiple counting of interactions by culling protein sequences in PDB by sequence identity using PISCES [The web interface, created in html and php, provides the user with a friendly and easy to use form for entering query data and selecting the filtering parameters Fig. . A searcg PISCES . The defg PISCES .Fig. 2ThTo identify the amino acids and atoms that surround a given ligand AFAL makes use of the spatial analysis functions of the VMD software . Using aThe AFAL output consists of a frequency table and an accompanying interactive histogram Fig. . All amihttp://structuralbio.utalca.cl/AFAL/index.html. AFAL is supported by Center of Bioinformatics from the University of Talca and will be constantly updated and maintained to ensure reliable operation even when some of the underlying tools are changing.The AFAL service is freely available for noncommercial use at To demonstrate the utility of AFAL to identify amino acids relevant in the coordination of particular ligands, a well characterized protein–ligand interaction was chosen as an example. ATP has essential roles in all forms of life. Characterization of the interaction of this molecule with specific amino acids is of great importance for understanding enzymatic mechanisms and for drug design. The ATP molecule is composed of an adenine base linked to three phosphate groups via a ribose. When bound to proteins, one or more magnesium ions are often found in coordination with the negatively charged phosphate groups. ATP is a multifunctional nucleotide used for many biochemical reactions that require energy via hydrolysis of the γ-phosphoester bond  and partSeveral studies have been carried out to characterize the amino acids involved in recognition of the phosphate groups and the adenine moiety. Recognition of phosphate groups requires the consensus sequence of GXXXXGKT(S), with serine substituting threonine in some cases . This mohttp://structuralbio.utalca.cl/AFAL/faq.html) showing overall frequencies of occurrence of each amino acid in proteins in general and/or in particular protein families (Table S1).In the November 2013 PDB release, there were 768 ATP-binding protein entries interacting with different protein families in a range of distances, the most common of which are the Transferases with 268 proteins  and Ligases with 104 proteins  . These AAs shown in Fig. Table A reference table that summarizes the most common contact types made by the amino acid and the most common functional groups from ligand atoms included for comparative purposes in supplementary Table S3 and is also available for on-site consultation by interested users at the bottom of the AFAL results page.Additional examples of the use of AFAL are provided in the web site.AFAL offers an automated solution for the analysis of interactions between proteins and their ligands across protein families and phylogenetic backgrounds using crystallographic data stored in the PDB database. The results obtained from AFAL provide valuable statistical information about the amino acids, atoms and distances that may be responsible for establishing any particular ligand–protein interaction, helping to compare the ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. It is anticipated that AFAL will provide an excellent opportunity to extract valuable information on the evolution of protein–ligand interactions and help suggest functions for unknown proteins containing potential ligand binding sites.Supplementary material 1 (XLSX 33 kb)Supplementary material 2 (XLSX 103 kb)Supplementary material 3 (XLSX 76 kb)Below is the link to the electronic supplementary material.


Protein–protein interactions are at the core of all cellular functions and dynamic alterations in protein interactions regulate cellular signaling. In the last decade, mass spectrometry (MS)-based proteomics has delivered unprecedented insights into human protein interaction networks. Affinity purification-MS (AP-MS) has been extensively employed for focused and high-throughput studies of steady state protein–protein interactions. Future challenges remain in mapping transient protein interactions after cellular perturbations as well as in resolving the spatial organization of protein interaction networks. AP-MS can be combined with quantitative proteomics approaches to determine the relative abundance of purified proteins in different conditions, thereby enabling the identification of transient protein interactions. In addition to affinity purification, methods based on protein co-fractionation have been combined with quantitative MS to map transient protein interactions during cellular signaling. More recently, approaches based on proximity tagging that preserve the spatial dimension of protein interaction networks have been introduced. Here, we provide an overview of MS-based methods for analyzing protein–protein interactions with a focus on approaches that aim to dissect the temporal and spatial aspects of protein interaction networks. Protein–protein interactions are at the core of all cellular functions and dynamic alterations in protein interactions regulate cellular signaling . AccuratMass spectrometry (MS)-based proteomics has become an indispensable tool in modern molecular and cell biology research . In shotin vivo is affinity purification-MS  methods have been employed to map changes in protein–protein interactions after cellular perturbations. Analysis of peptide samples from affinity purification experiments has typically been performed using data-dependent acquisition methods (DDA). Due to the semi-stochastic precursor ion selection in DDA methods, the complete set of peptides can often not be reproducibly identified across all samples. In DIA methods, fragment spectra for the entire mass range are acquired by co-isolating precursor ions in isolation windows of selected m/z ranges. in vivo by forming reversible covalent bonds between adjacent amino acids, thereby providing a snapshot of the protein interactome  or the engineered ascorbate peroxidase (APEX) can be employed to overcome this problem and preserve the spatial dimension of interactions .E. coli that activates biotin to biotinoyl 5-AMP (bioAMP) in an ATP-dependent reaction , which subsequently recruits electron dense osmium, yielding electron microscopy (EM) contrast (Another enzymatic proximity tagging approach developed by the Ting laboratory uses an engineered ascorbate peroxidase (APEX) . APEX iscontrast . Based o2O2 for 1 min. The proximal proteins can subsequently be purified using streptavidin under denaturing conditions and identified by LC-MS/MS analysis  and therefore have a small labeling radius (<20 nm). It is worth mentioning that APEX can also be used to confirm the subcellular localization of target proteins using EM or fluorescent microscopy. To date, the applicability of APEX beyond the mapping of proteins in membrane-bound cellular organelles has not been demonstrated, and it remains to be addressed if APEX-based proximity tagging is suitable for analysis of interaction partners of individual proteins or protein substrates of PTM-catalyzing enzymes.In vivo chemical crosslinking, in which chemicals that form reversible covalent bonds are applied to cells before lysis to “freeze” protein–protein interactions can help to identify these interactions. The need to optimize the crosslinking procedure for different cell types and bait proteins hinders the routine use of this method for analyzing transient protein interactions. In addition to AP-MS, approaches based on protein co-fractionation combined with quantitative MS have been successfully employed to analyze transient protein interactions during cellular signaling. Spatially restricted enzymatic tagging approaches, such as BioID and APEX, preserve the spatial organization of protein interaction networks and enable analysis of protein interactions in insoluble structures, thereby complementing AP-MS. Importantly, these approaches do not enable a distinction to be made between interaction partners and non-interacting proximal proteins. Therefore, combining affinity purification and spatially restricted enzymatic tagging could help to produce a more accurate and comprehensive picture of protein–protein interaction networks of interest. This strategy has the potential to become a standard procedure for protein interaction studies, as has already been exemplified by a recent study that focused on chromatin-associated protein complexes (Mass spectrometry-based proteomics has delivered unprecedented insights into human protein interaction networks. To date, most studies have focused on mapping steady-state protein–protein interactions. Future challenges remain in the identification of transient and low affinity interactions during cellular signaling, as well as in understanding the spatial organization of protein interaction networks. Although affinity purification combined with quantitative MS-based proteomics is a powerful approach for the identification of dynamic protein interactions, transient and low affinity interactions, such as those induced by growth factor stimulation or cellular stress, are frequently lost. omplexes JY, SW, and PB prepared and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This work discusses bioinformatics and experimental approaches to explore the human proteome, a constellation of proteins expressed in different tissues and organs. As the human proteome is not a static entity, it seems necessary to estimate the number of different protein species (proteoforms) and measure the number of copies of the same protein in a specific tissue. Here, meta-analysis of neXtProt knowledge base is proposed for theoretical prediction of the number of different proteoforms that arise from alternative splicing (AS), single amino acid polymorphisms (SAPs), and posttranslational modifications (PTMs). Three possible cases are considered: (1) PTMs and SAPs appear exclusively in the canonical sequences of proteins, but not in splice variants; (2) PTMs and SAPs can occur in both proteins encoded by canonical sequences and in splice variants; (3) all modification types  occur as independent events. Experimental validation of proteoforms is limited by the analytical sensitivity of proteomic technology. A bell-shaped distribution histogram was generated for proteins encoded by a single chromosome, with the estimation of copy numbers in plasma, liver, and HepG2 cell line. The proposed metabioinformatics approaches can be used for estimation of the number of different proteoforms for any group of protein-coding genes. Genome sequencing  decipherFollowing the hypothesis of “one gene = one protein,” there should be at least ~20,000 nonmodified  human proteins. Taking into account products of alternative splicing (AS), those containing single amino acid polymorphisms (SAPs) arising from nonsynonymous single-nucleotide polymorphisms (nsSNPs), and those that undergo PTMs , 5, as mExperimental validation of protein species is limited by the analytical sensitivity of proteomic technology. This means that the sensitivity of the technology determines the ability to detect rare protein species. This limitation originates from the basic difference between genomics and proteomics . Genomic master proteins resembling at least one of the many possible proteoforms, coded by the gene and containing at least one MS-detectable proteotypic peptide. The sequence could be modified or nonmodified, so this means that the master protein could be present as a single protein or as a set of proteins. Master proteome of a single chromosome is the result of the identification and measurement of all master proteins encoded by the chromosome and expressed in the selected type of biological material. For experimental validation of proteoforms, the targeted MS analysis should be performed in order to probe candidate sequence alteration. The bioinformatic analysis of the diversity of protein species was anticipated to create the backbone for the future experimental exploration of the proteome space.The 100% coverage of protein sequence using bottom-up MS is not attainable; thus, it is impossible to detect all potential protein species expressed from the same gene. Generally, proteome investigations are focused on theThe number of different proteins comprising the human proteome is a core proteomics issue. Researchers propose numbers between 10,000  differenThe data was derived from neXtProt, which contains only human proteins and their modifications and sequence features . The neXOur assumption was that database extension and annotation are a constituent process, whose rate is mostly limited by the number of the researchers and annotators around the world. The rate is slightly dependent on the capacity of communication channel and information accessibility, as these were not changed too much for 10–15 years for the needs of PubMed or UniProt users. Therefore, the extension of the number of annotations in a certain database would generally be affected by the technology achievements, gained by increasing the sensitivity/throughput of the bioanalytical method.From the above, we proposed that the volume of representative data uploaded to UniProt  each yeaThe saturation in the number of annotations for genome-dependent SAPs, transcription-dependent ASs, and posttranslational-dependent PTMs is quite remarkable. While PTM determination depends upon the sensitivity of protein analytics, SAP and AS detection have virtually no limitations in sensitivity and are actively accumulated via large-scale projects . DespiteN represents the total number of protein encoding genes, AS is the number of species produced by alternative splicing, ASav is the average number of splice variants per one protein encoding genes, SAPav is the average number of nsSNPs, and PTMav is the average number of PTM events per one protein encoding gene.For estimating the potential number of proteins, three different cases of combination of PTM, SAP, and AS events were considered see –3)). Co). Co3)).). Co). CGenerally, SAPs are predetermined at the DNA level, and AS arises from modifications at the mRNA level, while PTMs occur at the protein level. These three processes cannot be viewed as independent events, given that there is an intrinsic relationship between the processes of gene expression, transcription, and translation, aimed at regulating and preserving a cell. Furthermore, enriching MS/MS searches through a database containing all possible combinations of protein variations would lead to combinatorial collapse, despite the type of approach used .N = 20,043), we estimate that in humans there exist 0.62 or 0.88 or 6.13 million protein species.The neXtProt ver. 2015_06) search for protein AS modifications revealed 21,921 AS variants in 10,519 protein-coding genes . The greatest number of modified forms  , 10.5 th15 , 10.5 >10−6 M .−12 M) and ultralow (<10−15 M) concentrations of proteins implies that quantifying protein in copy numbers rather than in concentration units enables comparison of transcriptomic and proteomic results [Assuming the ultrasensitive capacity of oligonucleotide analytics, it is instructive to consider that transcriptome research results are often determined based on copies of RNA molecules rather than concentrations . Operati results .C, reported as mol/L . The corresponding number of protein copies, N, in 1 L can be calculated out of concentration units as follows:RA represents the reverse Avogadro's number, 10−24 M [V represents the sample volume, m represents the protein content, and Mw represents molecular weight of the protein.Proteins are commonly quantified in the proteomics field  by the c 10−24 M , V repreFormulas  address −14 M [−16 M by irreversible chemical binding proteins from large volumes of biological samples [The triple-quadrupole mass spectrometer makes it possible to achieve 10−14 M , 29 sens−14 M . The sen samples   [−11 M [In the context of proteome width, the targeted approach is limited by the need to measure only proteoforms exhibiting species . Possibi species . Targete species  and canc species . For exan saliva . Another10−10 M) . The app) [−11 M  and ubiq) [−11 M . From thSince it was shown that the set of proteins encoded by any human chromosome constitutes a representative portion for the whole human proteome , high-, 8 copies per 1 µL of blood plasma and 105 copies per liver/HepG2 cell. The ascending portion of the curve reflects high- and medium-copied proteins, whereas the descending portion may be explained by either diminished proteome diversity in a biological sample or more probably the notion that the proteins cannot be detected due to low sensitivity of the analytical methods [−14 M to 10−18 M by irreversible binding of analytes [5 copies per cell or per 1 µL of blood plasma) were gained and quantitatively measured, with at least two proteotypic peptides in each type of biomaterial  and their distribution across the dynamic range  is ultimately required for planning the workload for the chromosome-centric Human Proteome Project.Future successes in human proteome exploration depend upon the ability to use bioinformatics methods to elucidate existing protein species and targeted MS analysis, high-throughput measurement, and high-performance algorithms for


Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood-based method to covary across protein–protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex, provided that the number of aligned sequences is greater than the average length of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.DOI:http://dx.doi.org/10.7554/eLife.02030.001 Proteins are considered the ‘workhorse molecules’ of life and they are involved in virtually everything that cells do. Proteins are strings of amino acids that have folded into a specific three-dimensional shape. Proteins must have the correct shape to function properly, as they often work by binding to other proteins or molecules—much like a key fitting into a lock. Working out the structure of a protein can, therefore, provide major insights into how the protein does its job.Two or more proteins can bind together and form a complex to perform various tasks; and solving the structures of these complexes can be challenging, even if the structures of the protein subunits are known. Now, Ovchinnikov, Kamisetty, and Baker have developed a method for predicting which parts of the proteins make contact with each other in a two-protein complex.Different species can have copies of the same proteins; but a copy from one species might have different amino acids at certain positions when compared to a related copy from another species. As such, when pairs of interacting proteins from different species are compared, there will be many positions in the two proteins that vary. However, if the amino acid at a position in one protein  varies, and the amino acid at, say, position ‘Y’ in the other protein also varies such that for any given amino acid at position Y there is often a specific amino acid at position X; positions X and Y are said to ‘co-vary’. Ovchinnikov et al. noticed that when a pair of amino acids (one from each protein in a two-protein complex) co-varied, these two amino acids tended to make contact with each other at the protein–protein interface.Ovchinnikov et al. used the new method to make predictions about the protein–protein interfaces in 28 protein complexes found in bacteria, and also to make a prediction about the interface between protein subunits in the bacterial ribosome. When these predictions were checked against the actual structures, which were all known beforehand, they were found to be accurate if the number of copies of each protein being compared is greater than the average length of the two proteins.Ovchinnikov et al. went on to predict the amino acids on the protein–protein interfaces for another 36 bacterial protein complexes with unknown structures, and to present models for four larger complexes. The next challenge is to extend the method to protein complexes that are found only in eukaryotes . Since the number of related copies for eukaryotic proteins tends to be smaller, there are fewer proteins to compare and it is therefore harder to detect ‘covariation’ when it occurs.DOI:http://dx.doi.org/10.7554/eLife.02030.002 Recent work has demonstrated the accuracy of coevolution-based contact prediction for monomeric proteins using a global statistical model  to distiIn contrast to this rich body of work for monomeric proteins, relatively little is known about the utility of such statistical models in predicting protein–protein interactions. The more general problem of predicting if two proteins interact with each other has been studied extensively using a wide variety of approaches . Amino aIn this study, we examine residue–residue covariation across protein–protein interfaces using a pseudo-likelihood-based statistical method. In a large set of complexes of known structure, we find that covarying pairs of positions are almost always in contact in the three-dimensional structure, provided there are sufficient aligned sequences. We find further that significant residue–residue covariance occurs frequently between physically interacting protein pairs but very rarely between non-interacting pairs, and hence should be useful for predicting whether two proteins interact. We use the pseudo-likelihood method to predict contacts across protein-interfaces for 36 evolutionarily conserved complexes of unknown structure and present structure models for four of the complexes particularly well constrained by these data.For a single protein family, it is straightforward to generate a multiple sequence alignment and subsequently identify covarying residue pairs. To identify covarying residue pairs between two proteins A and B is not as easy: only organisms that contain an ortholog of protein A and protein B contribute, and in generating the alignments the protein A and protein B sequences for each organism must be properly paired. To simplify the ortholog identification problem, we focus on pairs of genes with conserved chromosomal locations separated in the genome by fewer than 20 other annotated genes. We then build GREMLIN global statistical models for sequences in the paired protein families. The models have ‘one-body’ parameters for each amino acid at each position in the two proteins, and ‘two-body’ parameters for each pair of amino acids at each pair of positions in the two proteins. These parameters are obtained by maximizing the pseudo-likelihood of the observed sequence pairs, rather than their likelihood, which makes the quite formidable estimation tractable. In the following sections, we investigate the structural contexts of residue pairs with large values of these two-body coupling parametersWe began by studying residue–residue coupling parameters in the bacterial 50S ribosomal subunit—the largest evolutionarily conserved bacterial multiprotein complex with an atomic resolution structure. For each individual protein in the complex, we constructed multiple sequence alignments by querying the UniProt sequence database  for homoWe find that in the 50S ribosomal subunit only a small fraction of residue pairs coevolve, as indicated by coupling strengths . As the 50S results . These predictions should contribute to the determination of the structures of these biologically important complexes.The results with the 50S ribosome and the protein pairs in the benchmark suggest that interactions can be accurately predicted across protein–protein interfaces given a sufficient number of aligned sequences. In apo form . We developed a docking protocol that used the predicted contacts as distance restraints and sampled the space of physically plausible structures to generate models of the protein–protein complex. The model with the best restraint score had an interface that was within 4 Å (in root mean square deviation) of the native interface in 14 of the 18 cases and had more than half the native contacts in 16 of the 18 cases  had a known structure in the 18 cases . Two of 18 cases .10.7554/Taken together, these results suggest that in cases with small conformational change, the docking protocol can recover the entire interface to high accuracy and in cases where binding is accompanied by a large conformational change, the protocol recovers the largest intact and/or unobstructed interface.Of the complexes with unknown structure listed in The tripartite ATP-independent periplasmic (TRAP) transporters are composed of three proteins: two integral membrane proteins YIAM and YIAN, and one periplasmic protein YIAO . The strTripartite efflux complexes span both the inner and outer membrane, and are widely used in bacteria to pump toxic compounds out of the cell. The mode of interactions between the outer membrane factor and the membrane fusion protein is unresolved, with reports suggesting either a tip-to-tip interaction, the insertion of one into the other, or a multistage interaction with an initial tip-to-tip interaction, followed by sliding one through the channel of the other . We genePyruvate formate-lyase (PFL) catalyzes the reaction of acetyl-CoA and formate from pyruvate and CoA in the Fermentation pathway. Formate acetyltransferase 1 or Pyruvate formate-lyase 1 (PFLB) is activated by Pyruvate formate-lyase 1-activating enzyme (PFLA). The structure of the complex is unknown, but the structures of the individual proteins have been solved . We correct for differences in sij due to sequence variability at different positions using the row and column averages of these values:To reduce the wion APC, . Unlike length L ; SI).As illustrated in We converted coupling strengths into residue-pair specific distance restraints and included them in the Rosetta structure prediction program. We use sigmoidal distance restraints of the form:straints  between Comparative models were built using RosettaCM  based onThe Rosetta ab initio protocol consists of two stages: in the initial stage (‘centroid’) side-chains are represented by fixed center-of-mass atoms allowing for rapid generation and evaluation of various protein-like topologies; the second stage (‘full-atom’) builds in explicit side-chains and carries out all atom energy minimization . YIAM, aapo form. In cases where the structure was of a homologous protein  and where most of the interface residues were present, we generated a structural model of the target protein using comparative modeling. We only considered cases where at least one of the structures was unbound as the bound–bound docking problem is not representative of real world docking challenges   to generFor fraction of native contact (Fnat) and interface root-mean-squared deviation (iRMSD) calculation, the interface residue–residue contacts are those where the minimal distance between any heavy side-chain atom is less than 5 Å. The Fnat calculation is performed as described in All structural figures were drawn with PyMOL .http://gremlin.bakerlab.org/complexes/ along with a web-server for paired-alignment generation, coevolution analysis and contact prediction/Rosetta restraint generation. The paired-alignments along with the PDB coordinates of the predicted structures are also available at Dryad: The multiple sequence alignments used in the analysis and the full GREMLIN results for all the calculations described in the paper are provided at  review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and 2 reviewers. To minimize delays, you should first respond by email to describe how you plan to revise the article before submitting a full revision. We are hopeful that by your submitting a detailed response this can be discussed by the editors and the reviewers, and that a revised manuscript need not then be submitted to re-review.Thank you for sending your work entitled “Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information” for consideration at The Reviewing editor and the reviewers discussed their comments, and there is consensus that this is an interesting and potentially very influential manuscript using evolution methods to improve both protein structure prediction as well as protein-protein interactions. The manuscript describes the application of the pseudo-likelihood approach, first described by 1) Why were structural models built only for the unknown complexes, and why not for the ones with known structure? Quantitative results for at least some of the complexes with known structure would be helpful to better understand the accuracy of the resulting models and the impact of distance constraints.2) There is not sufficient quantitative evidence demonstrating that the method described here is far superior to previous methods such as mfDCA and plDCA.eLife are most likely biologists, it might be useful to add a few sentences in the Introduction about the innovative nature of the pseudo-likelihood approach, and why is it better than the earlier methods. More generally, there are many places in the paper where some more explanation would help the readers. Note that eLife has no limitation on the length of the paper, and hence it is possible to improve readability.3) It is somewhat unfortunate that the paper as currently written overstates the novelty of the work, including unnecessary and unsubstantiated criticism of previous methods. Understandably, the authors did not want to repeat the discussion of the algorithm given previously by Balakrishnan et al. and also briefly in their PNAS paper . However 1) Why were structural models built only for the unknown complexes, and why not for the ones with known structure? Quantitative results for at least some of the complexes with known structure would be helpful to better understand the accuracy of the resulting models and the impact of distance constraints.This is an excellent suggestion. We have carried out a detailed analysis of docking results on proteins of known structure, and we will include this in the revised version. Briefly, in 30 of the 33 cases where there were at least two predicted contacts with scores greater than 0.6, the docking model with the best gremlin score was within 2 Å RMSD of the experimentally determined complex structure. We will point out that this likely overestimates the accuracy of our models of the unknown complexes, however, since docking of bound (from the structure of the complex) structures generally is more accurate than docking of unbound structures.2) There is not sufficient quantitative evidence demonstrating that the method described here is far superior to previous methods such as mfDCA and plDCA.The demonstration that the pseudo-likelihood approach is quantitatively better than previous methods was presented in the two previous papers on this approach. This is not the focus of this paper, as should be clear from the Abstract. The focus of this paper is, instead, the demonstration that co-evolution data can be used to generate reliable models of a wide range of protein protein complexes of biological interest. We go very considerably beyond previous work on this topic, which has focused on the Sensor histidine kinase+Response Regulator (SK/RR) two-component systems.3) It is somewhat unfortunate that the paper as currently written overstates the novelty of the work, including unnecessary and unsubstantiated criticism of previous methods. Understandably, the authors did not want to repeat the discussion of the algorithm given previously by Balakrishnan et al. and also briefly in their PNAS paper . However, considering that the primary readers of eLife are most likely biologists, it might be useful to add a few sentences in the Introduction about the innovative nature of the pseudo-likelihood approach, and why is it better than the earlier methods. More generally, there are many places in the paper where some more explanation would help the readers. Note that eLife has no limitation on the length of the paper, and hence it is possible to improve readability.The contribution of this paper is not the development of the pseudo-likelihood method, but the study of a large set of protein complexes of significant biological interest. We will provide more explanation of the innovative nature of the approach in the methods section. Where necessary, we will refer to the relevant sections of our PNAS paper  for tech


A protein complex consists of two or more proteins that are linked together through protein–protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag  and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples. Ti.e., affinity-tagged protein). The affinity captured target protein is subjected to stringent washing to remove possible nonspecific protein interactions, and then purified by using elution buffer. The affinity purified sample is digested and desalted ahead of MS-based identification of interaction partners of target protein . In this approach, the endogenously expressed target protein along with its interaction partners is purified using a specific antibody and a matrix such as protein A/G beads ( protein . HoweverS-transferase (GST), hemagglutinin (HA), polyhistidine (His), maltose-binding protein (MBP), Myc, and streptavidin (Strep) [i.e., healthy and diseased tissues). The purified complexes are generally eluted by changing the pH or salt condition, or by binding competition with a tag-specific matrix based on the experimental conditions [A variety of tags are available for affinity purification and the most popular tags are DYKDDDDK peptide (FLAG), glutathione- (Strep) ,13,14,20 (Strep) . The affnditions ,13,14.MW of 1012.98 kDa) hydrophilic peptide with amino acid sequence DYKDDDDK. The FLAG-tagged target protein along with its interacting partners is purified by using anti-FLAG antibody specific to the FLAG epitope. The competitive elution could be performed using FLAG peptide. The FLAG tag can be removed from the purified target protein using the specific protease, enterokinase for further use of the target protein  [Here we describe two examples of tags (FLAG and His) used in single step affinity purification . The FLAnalysis) . On the nalysis) . In anotnalysis) .MW of 20.7 kDa) is a combination of calmodulin-binding peptide (CBP) and IgG-binding protein A tags, which were separated by a TEV (tobacco etch virus) protease cleavage site [et al. [MW 18.8 kDa), which is a combination of protein G and streptavidin-binding peptide (SBP) tags, with 10-fold increase in yield when compared with demonstrated TAP procedure. Tagging proteins with high molecular weight tags may affect the proper folding ability, functional activity, and binding capacity [The methodological development of single step tag-based purification resulted in an approach known as tandem affinity purification (TAP) . This apage site . This isage site . Howeverage site ,25. Burc [et al.  have shocapacity ,26. Theret al. [The original TAP approach has been demonstrated in the yeast system . The yeaet al.  have demet al. . The AP-et al. .The next step is identification of interactors within the purified protein complex by MS analysis . Althougi.e., sheath gas) assists in droplet evaporation and ejection of peptide ions at high voltage. The peptide ions were then transferred to the mass analyzer , orbitrap, ion trap, or hybrid type) that is placed in high vacuum. The analyzed ions were subsequently recorded as mass spectra by the ion detector. The raw data obtained from the MALDI- or ESI-MS instrument is searched against the database and proteins of the protein complexes were identified [Although a variety of commercial mass spectrometers are available, the basic principle is similar for MS-based protein analysis. We recommend to readers other excellent reviews on basic principles of MS instrumentation ,43,44, w18O, ICAT approaches) can be performed by using the commercially available labeling reagents. However, recent advances in MS analysis and software development for data analysis has made the label-free quantitation (LQ) a powerful technique in quantitative proteomics [In addition to the identification of interactors within the protein complex, their relative expression levels can be quantified between two or more biological conditions by using quantitative MS analysis. Two types of MS-based quantitative approaches are available: labeling and label-free methods. Labeling can be performed at three levels to achieve accurate protein quantitation ,45. Metaoteomics . The LQ Labeling and label-free quantitation approaches have been widely used for the identification of contaminants within the protein complex ,20,31. Iet al. [et al. [In addition, labeling or label-free quantitation is useful for comparison of interactors of the protein complex between two or more biological conditions. In label-free approach  by prepaet al.  have shoet al. . In anot [et al.  have com [et al. .in vitro/in vivo experimental data and/or in silico-based predictions [The improvements in purification methods and developments in MS analysis tremendously increased the information related to protein interactions and their protein complexes. The experimental data related to protein complexes has been stored in a database known as the comprehensive resource of mammalian protein complexes (CORUM) ,57. In aComputational approaches are necessary to extract the biological significance by systematic analysis of information stored in the protein interaction databases. The data obtained from protein interaction databases could be analyzed and comprehended by using a variety of tools such as Cytoscape ,13. For The main types of pluripotent stem cells are embryonic stem cells (ESCs), and ESCs derived by somatic cell nuclear transfer (NT-ESCs), and induced pluripotent stem cells (iPSCs) generated by somatic cell reprograming ,70,71,72In addition to the above described protein interactions of core pluripotency genes, a recent study has demonstrated an interactome of RNA binding protein, L1TD1 (LINE-1 type transposase domain-containing protein 1) in human ESCs. The high levels of L1TD1 has been identified in ESCs and is involved in maintenance of human ES cells pluripotency. The AP-MS approach identified 306 proteins in the interactome of L1TD1 and several of them were known to be shared with Oct4, Sox2 and Nanog interactomes . This stTo gain further insight into pluripotency, the protein interaction data of Oct4 and Nanog from the above mentioned four studies ,75,78,79Identification of disease-associated genes  is critical for drug development, diagnosis, and therapy. The genes present in cancers but absent or expressed at lower levels in their corresponding healthy normal samples are known as cancer-associated genes. Typically, the cancer-associated genes/proteins were identified by conventional methods or omics approaches by looking into the fold-changes of gene expression levels between cancer and normal samples. However, the cancer-associated proteins interact with other proteins to initiate oncogenic signal transduction, which deregulates normal cellular functions and leads to cancer pathogenesis. Therefore, studying the protein complexes in cancers is important to understand the mechanisms associated with cancer pathogenesis.i.e., degradation of extracellular matrix) to promote tumor progression as well as in non-proteolytic functions  [n = 264) were identified in the nuclei of human breast cancer cells (MCF-7) [Here, we attempt to highlight some of the receptor protein complexes identified in cancer cells. Epidermal growth factor receptor (EGFR) is a well-known oncogene that plays a key role in multiple cancers. A recent study characterized its interactome in epidermal carcinoma cells by AP-MS analysis . This steration) . The interation) . This steration) . Ligand- (MCF-7) . Another (MCF-7) . Moreove (MCF-7) . The intBy reviewing the literature, we tried to emphasize the protein complexes identified in several cancer proteomics studies as follows: (i) the protein arginine methyltransferases (PRMTs) are deregulated in multiple cancers. It is known that more than 90% of the methylation of arginine residues is controlled by the activity of PRMT1, which is linked with transcription regulation . PRMT1 iin vivo/in vitro), computational analysis (in silico), and resource interaction databases are necessary to map direct and indirect protein interactions identified through the AP-MS approach. The quantitative AP-MS analysis has been used to identify the contaminants, which show non-specific interactions with the target protein. We believe that the major applications of quantitative AP-MS approach are to compare the interactors of the protein complex under different biological conditions and to study the protein interaction dynamics. However, a relatively large number of studies have demonstrated the static interactions of target proteins  but not the differential interactions and protein interaction dynamics of target proteins  [AP-MS is the most widely accepted approach to characterize protein complexes of target proteins due to the robustness and sensitivity of the MS analysis. Experimental or computational approaches have linked several protein complexes with cellular processes and molecular functions by using Gene Ontology annotations ,57,98. Tndition) ,48. It ii.e., protein localization-specific), cell condition-specific , and tissue-specific protein-protein interactions in databases. In addition, it is important to store the protein interaction dynamics and differential interactomes in databases. Moreover, the databases should include options to look into the above information for each gene and its variants. This may further enhance our understanding of the protein functional roles in pathogenesis as well as physiological conditions.The Encyclopedia of DNA Elements (ENCODE) project revealed 20,687 protein-coding genes in the human genome . In gene


Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97.Availability and implementation: Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred. The DoMo-Pred command line tool is implemented using Python 2.7 and C ++.Contact:gary.bader@utoronto.caSupplementary information:Supplementary data are available at Bioinformatics online. Protein–protein interactions (PPIs) are physical associations between protein pairs in a specific biological context. Their knowledge provides important insights into the functioning of a cell. Previously, experimental detection of PPIs was limited to labor intensive techniques such as co-immunoprecipitation or affinity chromatography (Multiple kinds of protein–protein interactions exist. We focus on interactions involving peptide recognition modules (PRMs), in particular Src homology three (SH3), which are important in many cellular signaling processes. These domains bind to small, linear sequence motifs (peptides) within proteins . SH3 domS. cerevisiae. A straightforward approach is to construct position weight matrices (PWMs) from phage peptides and scan the whole proteome for potential binding sites in target proteins using some threshold score .Position weight matrices (PWMs) are statistical models for representing sequence motifs. They are real valued S. cerevisiae often lie within the disordered regions of a protein (ip is the disorder score of the ith significant amino acid (either one for disordered or zero for ordered) and N is the number of significant amino acids in the binding site.PRMs bind to small peptide stretches containing a specific motif. Specifically interactions between proteins having SH3 domains and their targets are often mediated by proline rich peptide sequences containing PXXP, [R/K]xxPxxP, PxxPx[R/K] motifs. Proline disrupts the secondary structure of a protein by inhibiting the formation of helices and sheets . Also, s protein . DISOPREip is the surface accessibility score of ith significant amino acid and N is the number of significant amino acids in the binding site.Sequences present on a protein’s surface are more accessible to binding by SH3 domains than those that are buried inside a protein structure. The degree of solvent-accessible surface area of amino acid residues in a sequence indicates its level of exposure and is measured in terms of relative solvent accessibility (RSA)  are ideected by  is used ijc is the normalized contact area of the ith aligned domain and peptide residues of the jth base model. Alignment gaps in contact residues will negatively impact the average contact area as only the aligned residues are used for scoring . N is the number of aligned contact residues.Known 3-D structures of SH3 domains complexed with peptides can be used to assess the binding potential of a query SH3 domain and peptide by reducing residue-residue contacts in 3-D structures to a binary 2-D contact matrix . Six yeaa and b are the query proteins and C, P, F are the cellular component, biological process and molecular function ontologies.Physical PPIs require proteins to be in close proximity to each other i.e. they should co-localize in the same cellular compartment. Also, interacting proteins are more likely to be part of same biological process or have the same function. The Gene Ontology (GO) contains a hierarchy of controlled terms describing cellular location, biological process and molecular function of proteins . The funSupplementary Figure S1. Correlation coefficients from 86 gene expression profiles from GeneMANIA EX=1−ijp is the probability of seeing motif i on one protein and motif j on other protein in the experimentally verified PPI set, ip is the probability of seeing motif i and jp is the probability of seeing motif j in the same set.Sequence signature based PPI prediction methods are based on the notion that protein domains are correlated with specific functions. For instance, it has been shown that functionally related proteins have similar domain composition or they belong to the same ‘domain club’ .The objective of a Bayesian PPI prediction model is to estimate the probability that a given protein pair interacts, conditioned on the biological evidence in support of that interaction. A naïve Bayesian model simplifies this problem by assuming independence between different types of biological evidence. While modeling the PRM mediated PPI prediction problem a set of observations are made on domain-peptides while others are made on full-length proteins. Assuming that peptide and protein features are independent of each other, two separate naïve Bayes models  theorem  (see sup(P1) and an equal number of randomly selected negative interactions (N1). The protein classifier is trained on a high confidence set of 5215 pairwise yeast PPIs from the iRefIndex database (P2) and an equal number of randomly selected negative interactions (N2) .The goal is to construct a generalized model which can predict high confidence, in vivo yeast SH3 domain–peptide physical interactions. To achieve this, both peptide and protein classifiers are trained on their respective positive and negative datasets. The peptide classifier is trained on a high confidence set of 628 SH3 domain–peptide interactions in yeast from the MINT database Supplementary Fig. S2). Biological process (BP), cellular component (CC) and sequence signature (SS) outperform molecular function (MF) and gene expression (EX) in the protein feature set. Proteins could have the same molecular function but still belong to different processes and this could be one of the reasons behind molecular function feature’s weak performance. Gene expression data alone is not as powerful as others in discriminating positives from negatives  and accuracy (ACC). Peptide and protein classifiers with all features outperformed other models on at least one of statistical measure .Highly correlated features can negatively affect the performance of a naïve Bayesian classifier. Maximal information coefficient (MIC) is used to quantify the correlation between different features. DR and SA in the peptide feature set and CC and BP in the protein feature set are correlated with MICs of 0.72 and 0.5 respectively. The effect of correlation on classifier performance is measured by comparing different models without one of the correlated features. Further, to identify the feature subset which maximizes the performance of both classifiers, all possible combinations of features are compared using different statistical measures, such as area under ROC curve (AUROC), area under precision-recall curve (AUPRC), Brier score (BRIER), Mpep and protein Mpro naïve Bayesian classifiers. The majority of interactions in the P1 dataset are from two peptide array experiments . These PWMs are then used to predict SH3 domain–peptide interactions using the combined classifier. 534 unique PPIs (1481 binding sites) are predicted as positives for the stringent P-value PWM threshold of 1e-05 with no missing features (Supplementary Table S3). Approximately 55%  of these interactions are known at the PPI level (iRefIndex and MINT) and at least 172 (464 binding sites) out of 295 PPIs are known SH3 domain mediated interactions at the peptide level (with Supplementary Table S4). For example, some interacting partners of the MYO3 SH3 domain are found to be enriched in PI3K/AKT signaling. AKT is known to regulate actin organization and cell motility during endocytosis (Supplementary Table S5). These examples support our results and suggest that our predicted interactions are biologically relevant.30 PWMs representing multiple binding specificities of 25 SH3 domains in yeast are constructed using phage display data from nt  features, including presence in a disordered region of the protein, surface accessibility, conservation across different yeast species, and structural contact with the SH3 domain, as well as protein features such as cellular proximity, shared biological process, similar molecular function, correlated gene expression and sequence signature. Two separate Bayesian models are used to combine peptide and protein features. Their respective posterior probabilities are further combined using Bayes rule for predicting high confidence interactions. The combination of peptide and protein models achieved a higher accuracy of 0.97 compared to individual models on a curated benchmark dataset from


Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions. Depending on the phosphorylation state of the receptor, this complex is further directed to either desensitization or signalingβ-arrestins interact with many different protein partners, and that these interactions depend on the β-arrestin activation stateβ-arrestins, their role is probably to assemble signaling modules, allowing the different elements to remain in close contact for long periods, thus modifying the dynamics of biochemical reaction cascades.Most G Protein-Coupled Receptors (GPCR) have been demonstrated to transduce extracellular signals through both G protein-dependent and G protein-independent pathways. Amongst the G protein-independent pathways, those depending on the β-arrestins67β2-adrenergic receptor, G -protein-independent ERK phosphorylation is now generalized to many other receptors2This is notably the case for the Raf/Mek/Erk cascade. Indeed, this cascade exists in the cytoplasm, and is triggered by second messengers following the activation of Gα subunit. In addition, these three proteins have been shown to interact directly with β-arrestins, it is essential to determine which partners can interact simultaneously and which are mutually exclusive. To achieve this goal, one way is to test experimentally different combinations of purified partners. This process is time-consuming and the available experimental methods have many drawbacks. Moreover, the combinatorial complexity which would have to be explored precludes a systematic study. A second way is thus to predict the geometry of the possible assemblies using protein-protein docking and then to validate experimentally the predictions. More generally, such a modeling pipeline would allow to understand the functioning of so-called “hub” proteins. Apart from β-arrestins, other essential proteins like PP2A (protein phosphatase 2A), 14-3-3, p53 or β-catenin are also considered as hubs.Since many different proteins compete to interact with The principle of protein-protein docking is to determine the best association geometry from the 3D structures of the individual partners. In contrast to protein-ligand docking methods, because of the very large number of atoms in the ligand, protein-protein docking methods do not have a sub-ångström precision, and solutions having an RMSD (Root Mean Square Deviation) under 10 Å with the native conformation are generally considered satisfactory, and will be denominated near-native. A second aspect that should be highlighted is that protein-protein docking methods try to predict the best assembly of two proteins, but do not predict whether this assembly exists or not, and scores are generally not correlated with affinity. To this aim, a very large number of putative conformations of the assembly is generated  and scored, to extract a reduced number of highly probable conformations (from 5 to 20). The performance of a docking algorithm is determined by its ability to place near-native conformations within this final reduced set of conformations. Because we are interested in predicting the assembly of transient multi-partner complexes, we have developed PRIOR (Protein-Protein cOmplexes 3D structure pRediction), which allows to generate such models in a reduced time (each docking experiment requires a few hours of computation time), with high accuracy (a near-native solution is found in most cases), although giving coarse-grain models. The guideline is to allow the rapid prediction of large macromolecular assemblies that can be further validated, either by extracting data from literature, or experimentally.β2-adrenergic receptor/β-arrestin/Erk module complex. Four out of the five individual partners of the complex  have each been docked individually on both β-arrestins. The corresponding complex was then constructed using these individual dockings.We have used this new method to model the 3D arrangement of the a priori difficulty of the docking  and the pre-determined size of the solution set .The PRIOR algorithm consists of three successive steps. First, the candidate conformations are generated using Hexin vivo, and consequently do give solutions for any pair of proteins.When applied to a benchmark of 108 complexes of known three dimensional structures (3D structures), Hex generated near-native solutions see  for 92 cOne important feature of the 20 best-ranked conformations, is that the interaction regions they represent are very similar and centered on the real regions . This meSince PRIOR allows us to build near-native conformations for most heterodimers, it should be possible to build larger complexes just by assembling the docking solution heterodimers. To validate this assumption, we searched for complexes with known 3D structure, and for which the structure of the different partners isolated are also known. We found 5 such complexes see . In each10β-arrestin partners interact on the protein, we modeled a complex crucial in GPCR signaling: the receptor/β-arrestin/ERK module complex, containing the receptor, β-arrestin, c-Src, Raf, MEK and ERK. To model this complex, we docked the receptor, c-Src, Raf and MEK individually on the β-arrestin  are given in In order to challenge the potential of our method to predict where the plethora of arrestin . Each paβ-arrestins and c-Src. Since it is known that c-Src interacts with β-arrestin mainly through its SH3 domain12β-arrestin-dependent ERK activation is Src-dependent1214β-arrestin 2 docking gave satisfying results. In the docking model, the interaction of the SH3 domain with β-arrestin is very similar to the canonical interaction of SH3 domains with peptides. For example, β-arrestin in the vicinity of the SH2 domain to allow the kinase domain to contact the β-arrestin directly.We have first modeled the interaction between the two β-arrestin. Thus, we docked the two domains of Raf on the two β-arrestins. The kinase domain of Raf tends to dock to the receptor interaction region of both β-arrestins, and were thus rejected. In the docking model of the β-arrestin 1/Raf-RBD complex . To our knowledge, there is no experimental evidence showing which domain of Raf1 interacts with  complex , R307 ofwith Raf  and B.D. To ourβ-arrestins was demonstrated by GST-pulldown . Moreover, the N-terminal part of Mek is unstable and is disordered in most 3D structures. Residues S218 and S222 in Mek1 are solvent-accessible, and their location is compatible with phosphorylation by the kinase-domain of Raf1Mek1 is known to interact directly with rrestin 1. Whereasstin see . Howeverβ-arrestins. We therefore decided to dock Erk1 on the complex β-arrestin 1/Mek1. The model is compatible with experimental data , essential for Erk interaction. Thus, our hypothesis is that these two residues do not interact directly with Erk, but are necessary for the conformation of the interaction region.The last member of the signaling complex is Erk. Erk1 showed a clear preference for the Mek binding site in both tal data , Fig. 3Dtal data , thus alβ-arrestin/Raf docking complex validation, we designed two peptides, one from β-arrestin and one from Erk2, in order to experimentally validate our prediction. The GST-pulldown experiments confirmed this prediction , and clathrin as it was described in the crystallographic structure of the complex β-arrestiβ2-adrenergic, 110 for α2-adrenergic, 100 for D1A dopamine receptor, etc.), and consequently the region immediately beneath the receptor could be much more crowded. Taken together these facts could explain how the substrates of ERK are receptor-dependentβ-arrestin-dependent activation pathway of ERK is common to many receptors.One of the most interesting hypotheses arising from this model is that in the complex, the protein substrates of ERK would have to be close, and probably in contact with the receptor. Indeed, the active site of Erk is oriented towards the receptor, and the distance is approximately 55 Å which corresponds to the average size of a globular protein . FinallyWe have developed a new protein-protein docking method based on a coarse-grained model of protein structure and shown that taking into consideration structural and functional aspects of the individual partners and the desired size of the result set is essential. PRIOR gives near-native solutions in 80% of cases. Protein-protein complexes can be classified from their functional roles. The most homogeneous functional category contains enzyme-substrate complexes, and this is where the best results are obtained. This indicates that in the future, as the number of complexes of known structure will increase, we will be able to define more homogeneous functional classes leading to an improvement of prediction accuracy. We have also shown that this very high accuracy allows us to predict the 3D structure of multi partner complexes.β-arrestin/Erk module complex. The model obtained is compatible with almost all of the available experimental data. To obtain this model we made five different dockings: β-arrestin + c-Src, β-arrestin + Raf1, β-arrestin + MEK, β-arrestin-MEK  + ERK, and β-arrestin + receptor and we reassembled the retained conformations. This shows that, within a large macro-molecular complex, one pairwise interaction can be accurately predicted without the knowledge of the other partners. Using this model, we designed peptides mimicking the interaction regions of the β-arrestin/Raf complex on each partner, and the interaction regions of the β-arrestin/Erk complex on each partner. In both cases we were able to disrupt the interaction experimentally, thereby confirming these predicted interaction regions. The model is also compatible with the interaction with clathrin, and consequently internalization. Finally, the model leads to interesting hypotheses which could explain why GRK2/3 phosphorylated receptors do not produce β-arrestin dependent phosphorylated ERK, or how receptor specificity is translated into specificity of β-arrestin-activated ERK for selective substrates.Using this method we modeled the 3D structure of the receptor/Overall, these results show that our analysis pipeline is especially pertinent for understanding the interactome of hub proteins, which play crucial roles in cells. This also shows that despite a very large number of partners, the hub protein binds to each of them with great specificity, and the interaction regions can be efficiently predicted from the structures of isolated partners. In particular this should help to determine which proteins are in competition for the same site and mutually exclusive (XOR), which importance was demonstrated recentlyThe candidate conformations were generated using HexHigh quality (4): fnat > 0.5 AND (Irmsd < 1 OR Lrmsd < 1);Medium quality (3): [fnat Acceptable (2): [fnat > 0.3 AND Irmsd > 2 AND Lrmsd > 5] OR [fnat Correct (1): fnat < 0.3 AND (Irmsd < 5 OR Lrmsd < 10);We distinguished 4 categories of near-native conformations. Three of these categories were used as defined in the CAPRI experimentHere fnat is the fraction of native contacts that is predicted, Irmsd is the root mean square deviation between the model and the native structure restricted to the interface amino acids, and Lrmsd for the complete Ligand chain, which is by convention the smallest partner, the Receptor chains of the model and the native structure being superimposed. Conformations not belonging to any of these 4 categories are named incorrect. Conformations belonging to one of these categories are named near-native.min(xi) and max(xi) represent respectively the minimum and maximum values observed for attribute xi in the learning set. Missing values were replaced by the maximal value observed in the learning set for the same attribute, except for the functions trained on Enzymes for which a missing value was replaced by the median of all values observed on the set for the same attribute.The Voronoi-based coarse-grained model and attributes are defined ini under the non-specific function number j (30 functions), i under the specific function number j (30 functions). The ranks of the best-ranked conformation were used for normalization.The positive learning set originates from the one defined inFor each complex of the benchmark, the scoring functions were learnt using a leave-one-out procedure .Our previous studies have led us to reconsider the level of difficulty indicated in the benchmark, which did not correlate well with our ability to correctly predict the structure of the complex. Moreover, this difficulty level relies on the differences between bound and unbound conformations, and is thus not accessible for unknown complexes. After a thorough analysis of different structural features of the individual partners, we have concluded that the most important factors that impair the prediction accuracy are: the multimeric state of individual partners; the presence of a solvent-accessible cofactor; and the flexibility of the partners as evaluated by comparing the existing structures of the same protein or close homologs. We therefore designed a new classification according to these factors. A complex presenting none of these factors was classified as easy (36 complexes), a complex presenting one of them was classified as medium (44 complexes), and a complex presenting two or more was classified as difficult (12 complexes).xi, wi and ci are the weight and centering value respectively, optimized by learning procedures.Using training attributes described inμ scheme has been used, with λ = 20 parents μ = 120 children, the maximal number of generations was set to 500, and classical cross-over operator and auto-adaptative mutation were used. Following the genetic algorithm optimization, the resulting parameter sets  were further optimized using CMA-ES (Covariance Matrix Adaptation - Evolution Strategy)The fitness function used in the genetic algorithms is the area under the ROC curve (Receiver-Operator-Characteristic). A λ + i of both partners was assigned a value Vi computed as the sum of the ranks of the candidate conformations in which this amino acid belongs to the interface, using a given scoring function. For each candidate conformation, the A-score is the sum of the Vi. For each complex and for each candidate conformation, the C-score was computed as the sum of its own rank and the ranks of the other candidate conformations sharing an RMSD lower than 5 Å2 with it, and which ranks are higher than its own rank. Different combinations of the A-score, C-score, Hex rank and consensus rank were used depending on the functional category, the a priori difficulty and the chosen size of the result set  as the prey. Interfering peptides were designed according to the predicted docking and were synthesized by Genecust laboratories . They corresponded to 9 and 10 amino acid-long sequences located at the interaction interface of either β-arrestin 1, Raf1 or ERK2. The control peptides corresponded to either a sequence of β-arrestin 1 located outside of the predicted interaction site or a sequence of a completely different protein  for 1 hour at 4 °C. GST beads were then washed six times in binding buffer and resuspended in 25μL of 2X Laemmli buffer. Equal amounts of samples were separated in a 10% acrylamide gel by SDS-PAGE. Membranes were incubated overnight with polyclonal anti-β-arrestin primary antibody  and then with IRDye Alexa fluor 680 secondary antibody . Protein loading homogeneity was confirmed by stripping, washing and reprobing membranes with polyclonal anti-GST antibody for GST-raf1-RBD  and with anti-ERK antibody for GST-ERK2 . Imaging and quantications were carried out using an ‘Odyss’ey CLX scanner (Licor).Raf1-RBD cDNA inserted in pGEX was kindly donated by Dr V. Fafeur  and GST-ERK2 purified protein was provided by Interchim . GST-Raf1-RBD and GST-ERK2 fusion proteins were expressed in NEB 5-alpha competent Escherichia coli , as previously describedtein see . Equal aStatistical analysis of the data were performed with one-way ANOVA (Bonferroni’s multiple-comparison tests) to compare samples using PRISM software.How to cite this article: Bourquard, T. et al. Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex. Sci. Rep.5, 10760; doi: 10.1038/srep10760 (2015).


Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways. In this work, we propose a simple feature construction method to train a SVM (support vector machine) classifier to predict PPI upstream/downstream relations. The domain based asymmetric feature representation naturally embodies domain-domain upstream/downstream relations, providing an unconventional avenue to predict the directionality between two objects. Moreover, we propose a semantically interpretable decision function and a macro bag-level performance metric to satisfy the need of two-instance depiction of an interacting protein pair. Experimental results show that the proposed method achieves satisfactory cross validation performance and independent test performance. Lastly, we use the trained model to predict the PPIs in HPRD, Reactome and IntAct. Some predictions have been validated against recent literature. Signal transduction plays important roles in the life processes of cell, e.g., growth, differentiation, metabolism and apoptosis. Malfunction of signaling pathways would lead to a variety of pathologies235678910et al.et al.et al.et al.At present the computational methods that infer signaling pathways focus on predicting novel signaling components or pathway targeted proteins1315171819et al.F to predict the direction of any domain pair, and the authors further proposed a parameter PIDS to predict the direction of any protein pair. As compared to those PPI network topologies based methods, this method effectively exploit the experimentally verified upstream/downstream information between interacting proteins. But the upstream/downstream information of two domains may not be sufficient to determine the upstream/downstream relationship between two proteins. The rules may not be so simple. The directional determinant may be highly non-linear domain combinations. For instance, upstream/downstream relationship between two proteins is potentially determined by two up-regulating domains from one protein and one down-regulated domain from the other protein. Therefore, nonlinear combination of up-regulating domains and down-regulated domains should be taken into account for prediction of upstream/downstream relations between interacting proteins.Liu A, B) is represented with two instances In this work we propose an asymmetric domain feature representation method to train a nonlinear SVM (support vector machine) to annotate the upstream/downstream relations of signal flow in human protein-protein interaction networks. Using this feature representation, the relations between the up-regulating domains and the down-regulated domains can be easily incorporated into the feature vectors, and the trained SVM easily maps the nonlinear domain regulatory combinations to regulatory direction between interacting proteins. However, the intuitive feature representation makes the final decision complicated, i.e. each protein pair .We have collected 1,148 annotated PPIs from 22 human signaling pathways in KEGGenzyme catalysis explicitly provides the information of PPI upstream/downstream relations. The PPIs of enzyme catalysis are directional and can be used as independent test set. We collect 730 PPIs of enzyme catalysis  and 3,216 physical PPIs  with no directional information from 18 human cancer signaling pathways . NetPath-PI will be annotated using the proposed predictive model.NetPathHPRDet al.http://reactomews.oicr.on.ca:8080/caBigR3WebApp2014/FIsInGene_121514_with_annotations.txt.zip) and is suited to be used to validate our proposed model . Nevertheless, the entries in this dataset are provided without reliable sources, so we do not incorporate this dataset into the training data. Actually, Wu et al.http://www.uniprot.org/) are removed.HPRD is specialized to collect physical protein-protein interactions of Homo sapiens. Comparatively, ReactomeFor clarity, the data flow chart for model training, evaluation and prediction is illustrated in et al.Protein domain or domain-domain interaction has been used to predict the directionality between two interacting proteinsg . Here we only focus on undirected is not needed. For the aforesaid reasons, we, similar toM by 20-fold cross validation on the training data, based on which to further calculate several intermediate variables as formula (4), SPl, SEl and MCCl for each label as formula (5), and overall Accuracy & MCC as formula (6).Now we have constructed the positive data The confusion matrix The micro instance-level performance metrics are used to estimate the basic performance that discriminates the positive instances negative class is semantically defined as NOT experimentally verified or unlikely to exist. Nevertheless, we can still semantically interpret the case Where Based on the combined output Based on formula (9), the macro bag-level performance metric is defined as follows:Macro_accuracy denotes the coincident rate between the predicted directions and the true directions.where T denotes the set of experimentally verified PPI upstream/downstream relations. et al.et al.Comparatively, Liu Here the classifier SVM assumes RBF  kernel as defined below:https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/) is adopted and the hyperparameter pair where macro_accuracy is also promising, indicating that the proposed method correctly recognizes 74.12% PPI upstream/downstream relations.First, we conduct 20-fold cross validation on KEGG that contains 893 positive instances and 893 negative instances (see the previous section feature construction), i.e. Phase I as illustrated in macro_accuracy indicates that 75.87% PPI upstream/downstream relations are correctly recognized. Liu et al.To take full advantage of the currently available data, we further merge KEGG with NetPath-EC into a larger training data KEGG + NetPath-EC to train the final SVM for network-wide prediction, i.e. Phase II as illustrated in macro_accuracy shows that 74.54% NetPath-EC PPI upstream/downstream relations  to evaluate the model that is trained on KEGG  by GSK3 is inhibited by PPPSPxS motif peptides or by phosphorylated LRP6 cytoplasmic domain (GSK3B--->CTNNB1). InSimilarly, the hub proteins, e.g. {CTNNB1, GSK3B, DVL1, AXIN1}, are predicted to be heavily targeted or to target many other peripheral proteins as illustrated in Protein-protein interaction (PPI) networks are important infrastructures to infer signaling pathways, based on which to further understand the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. However, the current PPI networks do not carry the upstream/downstream information of two interacting proteins, preventing us to gain knowledge about signal flows in PPI networks. At present the experimentally derived PPI upstream/downstream relations are scarcely collected in such the repositories as KEGG, NetPath etc. Computational modeling is a cheap and efficient approach to the reconstruction of signal flows in PPI networks. To date there are a very few computational methods to annotate the current PPI networks with upstream/downstream information. Most of the existing computational methods heavily depend on PPI network topologies to predict the signal flows from membrane receptors to nucleus transcription factors. However, overdependence on PPI network topologies has the drawbacks (1) the network is incomplete and contains a certain level of noise so that the shortest paths are inaccurate; (2) the shortest path algorithm cannot tackle the signaling feedback loops and the cross-talks between signaling pathways; (3) the experimentally verified PPI upstream/downstream relations are not exploited The domain based method derives statistics of domain-domain upstream/downstream relations to predict PPI upstream/downstream relations. Simple as it is and it does not depend on PPI network topologies, the method cannot tackle non-linear domain combination. Similarly, the method neither exploits the experimentally verified PPI upstream/downstream relations for model training.In this work, we propose a simple feature construction method to incorporate the domain-domain upstream/downstream relations, based on which to train a nonlinear SVM classifier to predict PPI upstream/downstream relations. Differently the proposed method does not depend on PPI network topologies but directly exploit the experimentally verified PPI upstream/downstream relations in KEGG and NetPath. For each protein pair , there are two basic directions i.e. et al.Lastly, we use the trained model to annotate the PPIs in HPRD, Reactome and IntAct. Nevertheless, we cannot annotate the whole PPIs in the three databases for two reasons (1) not all proteins contain already known pfam domains; (2) the training data is relatively small, so that the coverage of pfam domains is rather limited, thus the proteins that do not contain the signature domains of the training data should be removed. For the reasons, HPRD is reduced to 19,736 PPIs, Reactome is reduced to 36,242 PPIs and IntAct is reduced to 17,351. The predictions of Reactome and IntAct are provided in Section 5 and Section 6 of the et al.89To enlarge the coverage of domains is the key factor for our proposed method to gain wide applicability. In our future work, we will solve the problem from the two aspects (1) augment the training data; (2) transfer the domains of the homologs to the target proteins. The training data can be augmented by borrowing some data with reliable sources that are collected by Wu How to cite this article: Mei, S. and Zhu, H. A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks. Sci. Rep.5, 17983; doi: 10.1038/srep17983 (2015).


In the current version, SMART contains manually curated models for more than 1200 protein domains, with ∼200 new models since our last update article. The underlying protein databases were synchronized with UniProt, Ensembl and STRING, bringing the total number of annotated domains and other protein features above 100 million. SMART's ‘Genomic’ mode, which annotates proteins from completely sequenced genomes was greatly expanded and now includes 2031 species, compared to 1133 in the previous release. SMART analysis results pages have been completely redesigned and include links to several new information sources. A new, vector-based display engine has been developed for protein schematics in SMART, which can also be exported as high-resolution bitmap images for easy inclusion into other documents. Taxonomic tree displays in SMART have been significantly improved, and can be easily navigated using the integrated search engine.SMART (Simple Modular Architecture Research Tool) is a web resource ( Protein domain analysis remains an important research tool, made easy by various frequently used online domain resources and databases e.g. –3. The SSMART was never intended to be exhaustive, and was initially focused on mobile domains, which occur in various contexts while retaining similar function. Nevertheless, it continues to gradually expand its domain coverage with each new release. The current version introduces more than 200 new domains, bringing the total to 1204 distinct modules that can be detected. SMART's domain annotation includes a significant amount of manual work, in particular when selecting individual cut-off values and while creating the high quality underlying multiple sequence alignments. Other more exhaustive databases, like Pfam , alreadyThe main protein database in SMART consists of the complete UniProt protein database  combinedEukaryota, 1678 Bacteria and 115 Archaea), compared to 5 million proteins from 1133 species in the previous release.In addition to the regular protein database described above, SMART offers a ‘genomic’ analysis mode that contains only proteins from completely sequenced genomes. Synchronized with the upcoming STRING version 10  analysis. These information dialogs contain condensed versions of respective annotation pages, with links to the full annotation. The new viewer also includes an interactive protein size scale, which allows users to directly select any protein region and submit it to a BLAST service of choice.With the redesign of the protein annotation pages, the current version of SMART introduces two new external information sources: post-translational modification data and detailed orthology information Figure .Data on post-translational protein modifications is provided by version 2 of the PTMcode database , and is Protein orthology data are parsed from the eggNOG database  and coveWith the update of the underlying protein databases, we have also synchronized our protein interaction data with the latest version of the STRING database . GraphicSMART's integration of biological pathways data was greatly expanded in the current version, and is now synchronized with version 2 of the interactive Pathways Explorer (iPath2) . AvailabBiological pathway data, protein interaction data and their associated graphical representations are now part of SMART's new tabbed annotation interface Figure , displaySMART uses simple tree structures to display various taxonomic breakdowns in different parts of its user interface. For example, these are used to show the evolutionary information in domain annotation pages or to display the taxonomic breakdown of domain architecture queries. Since our current database contains proteins from more than 350 000 species, we developed a new tree display widget with an associated full text search engine Figure . It suppThe backend of SMART is a relational database management system, powered by the PostgreSQL engine, which stores the annotation of all SMART domains, protein annotation and sequences, taxonomy information and the pre-calculated protein analyses for the entire UniProt , Ensembl