Macromolecular visualization as well as automated structural and functional annotation tools play an increasingly important role in the post-genomic era, contributing significantly towards the understanding of molecular systems and processes. For example, three dimensional (3D) models help in exploring protein active sites and functional hot spots that can be targeted in drug design. Automated annotation and visualization pipelines can also reveal other functionally important attributes of macromolecules. These goals are dependent on the availability of advanced tools that integrate better the existing databases, annotation servers and other resources with state-of-the-art rendering programs.We present a new tool for protein structure analysis, with the focus on annotation and visualization of protein complexes, which is an extension of our previously developed POLYVIEW web server. By integrating the web technology with state-of-the-art software for macromolecular visualization, such as the PyMol program, POLYVIEW-3D enables combining versatile structural and functional annotations with a simple web-based interface for creating publication quality structure rendering, as well as animated images for Powerpoint™, web sites and other electronic resources. The service is platform independent and no plug-ins are required. Several examples of how POLYVIEW-3D can be used for structural and functional analysis in the context of protein-protein interactions are presented to illustrate the available annotation options..POLYVIEW-3D server features the PyMol image rendering that provides detailed and high quality presentation of macromolecular structures, with an easy to use web-based interface. POLYVIEW-3D also provides a wide array of options for automated structural and functional analysis of proteins and their complexes. Thus, the POLYVIEW-3D server may become an important resource for researches and educators in the fields of protein science and structural bioinformatics. The new server is available at  Computational tools for protein structure visualization, analysis and functional annotation are being constantly developed and improved in order to enable better integration with quickly evolving proteomic databases and other on-line resources, to facilitate and automate annotation tasks of ever increasing complexity, and to address the growing demand for high quality structure rendering. In regard to the latter, emerging new technologies gave rise to a number of advanced, stand-alone tools for macromolecular graphics, including the PyMol program  that comWhile stand-alone packages, such as MolScript , RasMol In this regard, the PDB2MGif server  should bThis new server represents a significant extension of our previous efforts to provide the proteomics community with a flexible web-based platform for protein structural and functional annotations . In partThe primary input data format for the server is the standard Protein Data Bank (PDB ) format vs. ligand) or their fragments  are available through expandable, context dependent menu boxes. Several highlighting and coloring styles are also provided through such context dependent menus, complemented by the possibility of specifying multiple styles  for each residue by using a user defined highlighting list, with a convenient style converter and list manipulation tool. In order to further simplify the use of the server, the initial orientation, centering, and zooming of the molecule can be pre-specified or set interactively by using the Jmol program embedded into the POLYVIEW-3D web-interface.For example, rendering using cartoon, wireframe, CPK, solid and transparent surface models, as well as their combinations for different molecules , a faster alternative that utilizes the RasMol program  is also libgd , often pWe would like to point out that POLYVIEW-3D represents a major update of the original POLYVIEW server. While POLYVIEW is cross-linked and somewhat complementary with POLYVIEW-3D, and some of the simpler functions of the latter were recently integrated into POLYVIEW as well, these two tools are different in at least two fundamental aspects. First of all, POLYVIEW does not allow one to generate high quality rendering of 3D macromolecular structure, including animation movies. Secondly, POLYVIEW does not offer advanced annotation and analysis options that deal with topographical and other 3D aspects of macromolecules. On the other hand, POLYVIEW-3D is specifically designed to provide these capabilities, e.g., in the context of analysis of 3D characteristics of interaction interfaces, as described in the next section.There are several different types of annotation options for structural and functional analysis that are available in POLYVIEW-3D. Some basic options that were mentioned before include: highlighting various amino acid properties and their distributions within protein structure; displaying crystallographic temperature factors (if available) to identify flexible regions; generating animated movies to visualize conformational ensembles that are represented by multiple models in structures solved using NMR, or to visualize molecular motion as represented by Molecular Dynamics trajectories, distortions along Normal Modes , or othSome more complex tasks, geared up primarily towards the interrogation of protein complexes and interaction interfaces, are achieved by combining several tailored tools, stand-alone software packages and web servers. These tasks include identifying and mapping known interactions found in protein complexes deposited in PDB, the assessment and mapping of evolutionary conservation onto the protein structure model, mapping and analysis of pockets in the structure as putative targets for ligand docking, and analysis and ranking of protein docking models. In particular, the DSSP program  is used Furthermore, by using the SPPIDER server , which iSpecifically, a custom PDB file with multiple models of a protein complex in the CAPRI format  may be nr protein database [The evolutionary conservation may be assessed using the output from the ConSurf server , which ydatabase . Such geAnother type of analysis, which is of interest in the context of drug design, is the identification of structural pockets and cavities in proteins, including those within interaction interfaces, as potential targets for docking studies. For that purpose, POLYVIEW-3D is coupled with the CASTp server , which iPOLYVIEW-3D does not require any plug-ins and is expected to be fully functional under any platform and with any recent Web browser. Moreover, an intuitive graphical interface alleviates the need to learn an often complex syntax of commands and options available in programs utilized by the server to generate images and annotations. At the same time, the web interface offers a number of options to customize the output and tailor the analysis of the system at hand. In addition, a script is provided for advanced users for download in order to improve the image by using locally installed rendering software. The POLYVIEW server also provides extensive cross-linking with other public annotation resources, such as the PDB  and NCBIBacillus subtilis operons involved in beta-glucoside metabolism [In this section, we illustrate how POLYVIEW-3D server can be used for protein structure analysis in the context of protein-protein interactions. In particular, we show several specific examples of structure rendering and annotation for a homodimeric complex of regulatory units of the transcriptional antiterminator protein LicT, which regulates the expression of tabolism . All optThe regulatory units of LicT consist of two five helical bundle domains called PRD1 and PRD2, which adopt dramatically different relative orientations in inactive and active forms of the protein . In the activated state, each PRD forms a dimeric unit with its counterpart in the other chain, burying, at the dimer interface, phosphorylation sites that are critical for regulation (conserved histidine residues). In the inactive state, a wide swing movement of PRD2 results in partial opening of the dimer, making the phosphorylation sites accessible on the protein surface . This inAs can be further seen from Figures We would like to comment that a relatively large, rather hydrophobic with some hydrophilic hot spots (data not shown) and relatively rigid (in this bound structure) pocket that involves some conserved residues, is identified in the central groove of the structure, essentially in between two distinct patches forming the interface. Given the importance of the dimerization state and interface formation for the function of the protein, this pocket may represent a valuable target for ligand design. We would also like to point out that POLYVIEW-3D can be used for additional analysis of conformational changes between inactive and active forms by generating animated movies and individual snapshots of putative trajectories representing transition between the two structures, as generated, e.g., by the AD-ENM server , or obtaIn Figures The correct ranking of these two models by ClusPro is certainly very encouraging. However, in general, multiple and often vastly different models from protein docking simulations are difficult to assess. Therefore, contrasting the results of protein docking approaches with predicted functional hot spots and interaction interfaces, provides a complementary approach to further improve model ranking and confidence in the models selected as top candidates. Using POLYVIEW-3D greatly facilitates such comparative analysis. In this particular case, as can be seen from the figures, only the first model shows significant overlap with predicted (from unbound structures) interaction sites, which are highlighted in red  and yellow (for the remaining predicted interaction sites).We would like to comment that SPPIDER predicts in this case two distinct interaction interfaces in the N- and C-terminal regions of the regulatory domain, coinciding with PRD1 and PRD2 subdomains. While only one of these interfaces is present in the inactive form analyzed here in detail, due to the rearrangement of the structure in terms of the relative orientation of PRDs, the other predicted patch overlaps, in fact, with the alternative interface observed in the active form of LicT. The latter can readily be verified using POLYVIEW-3D and the mapping of interaction sites from multiple complexes involving close homologs of the chain of interest, as described in the previous section.While this is just an example of an application of POLYVIEW-3D to simplify the analysis and visualization of protein docking models, and caution should be exerted to avoid over-interpretation of such results in any particular case, on average, similar level of accuracy was observed for other CAPRI targets . In the We present a new Web-based server, called POLYVIEW-3D, for versatile structure annotation and high quality visualization (including static views and animations) of macromolecules, with the focus on the analysis of protein complexes and protein interaction interfaces. POLYVIEW-3D integrates and greatly simplifies the use of programs that are employed for structure analysis and image rendering. In particular, the new server features the PyMol rendering program, which is coupled with several rigorously validated annotation and prediction servers, such as ConSurf, CASTp, ClusPro, and SPPIDER, providing an easy to use platform for gaining insights into protein structure and function, and facilitating common analysis and annotation tasks. The new server offers a number of advanced rendering options for preparation of figures and electronic materials, complementing stand alone and existing web-based tools in that regard.• Project name: POLYVIEW-3D• Project home page: • Operating system: Platform independent• Programming languages: Perl, JavaScript• Other requirements: Web-browser supporting W3C standards• License: The use of the server and its results are freely available for all users; However, in the interest of better serving the community with limited computational resources, the authors reserve the right to limit the number of submissions per unit of time• Restrictions to use by non-academics: Same as abovePDB, Protein Data Bank; NCBI, National Center for Biotechnology Information; DPI, dots per inch.JM and AP conceived of the study and designed the server functionality. AP implemented the server and JM performed structure analysis for use as an example in the paper. Both authors drafted the manuscript, read and approved the final version.


Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families. Genome annotation relies heavily on bioinformatics methods. The identification of homologous relationships is a powerful and frequently used approach for protein-level annotation , where qComplementary to sequence similarity searches, more direct approaches for the functional characterization of gene products have been proposed. In particular, genomic context methods predict which gene products are involved in common biological processes ,6. Otherbiological process, molecular function, and cellular component. The three ontologies are represented as directed acyclic graphs (DAG) in which nodes correspond to terms and their relationships are represented by edges. Each node can have several parents and several children. There are two types of relationships. "is-a" indicates that the child is a subclass of the parent, and "part-of" is used when the child is a component of the parent. GO terms are widely used to annotate genes and their products with functional terms . Since the relevance of a term decreases with increasing probability, the similarity is weighted with 1 - p(c) in the computation of simRel.Like GOA and GOB of GO terms with sizes N and M, respectively, a similarity matrix S is calculated. This matrix contains all pairwise similarity values of mappings The first step in the comparison of two gene products is the pairwise comparison of their GO mappings. The mappings to the different ontologies  are examined separately. Considering two gene products A and B annotated with the sets sij = simsimResnik, simLin, and simRel). The matrix S is not necessarily symmetric or square since the proteins can have different types and numbers of GO mappings. The rows and the columns of S represent two different directional comparisons, row vectors correspond to a comparison of A to B and column vectors of B to A. The best hits for the comparison of A with B are determined as maximum values in the rows in matrix S (row maxima). The maximum values in the columns of S (column maxima) are the best hits for the direction B to A. The averages over the row maxima and the column maxima give similarity values for the comparison of A to B and the comparison of B to A, respectively:The matrix may be calculated with any of the similarity measures mentioned above GOscore is the generic name for either MFscore if it is based on molecular function or BPscore if it is based on biological process. This score does not penalize situations where all GO terms of one gene product match a subset of the GO terms of the second gene product. This situation occurs when the annotation of the first gene product is not complete or when the second gene product is multi-functional.where funSim score is calculated from the MFscore and the BPscore of a pair of gene products. Two gene products with a high score in one ontology but only an average score in the other one can be considered average matches. However, their score should be higher than the score of two gene products that are average matches in both categories. Simply adding MFscore and BPscore or taking the average would not distinguish between these two cases. Squaring the MFscore and the BPscore favors high similarity in one ontology and a low score in the other one over average scores in both ontologies, thus allowing a distinction between these two scenarios. Therefore, the funSim score for two gene products is calculated as:The max(BPscore) and max(MFscore) denote the maximum possible score for biological process and molecular function, respectively. If simRel is used, the funSim score lies in the interval . We use simRel for our analysis throughout the article.Here, funSim. funSim can be applied to any type of gene product that is annotated with GO terms. Furthermore, it can be calculated with any semantic similarity measure that has a well-defined maximum.Lin's similarity is not a metric since it does not satisfy the triangle inequality. This also holds for Saccharomyces cerevisiae proteins and human proteins with a score of 1.0 have been extracted from each Inparanoid cluster. All yeast-human protein pairs where both proteins had biological process and molecular function annotation were used. In total 682 protein pairs were obtained.The set with Inparanoid orthologs (IO) was extracted from Inparanoid version 4.0 . SaccharFor the two sets of protein pairs with low sequence similarity (LSS) and high sequence similarity (HSS), a BLAST ,46 searcIn order to compile a set of protein pairs with no sequence similarity (NSS), all human proteins with biological process and molecular function annotation that are not in the IO set were selected. One of these human proteins was assigned randomly to each yeast protein from the IO set. The proteins had no significant sequence similarity. The NSS set contains 1356 protein pairs.simRel measure. For the comparison of proteins, the GOscoreLord was computed according to the following formula:We used the IO, HSS, LSS, and NSS datasets mentioned before for this analysis. The semantic similarity between single GO terms was calculated using the et al. [MFscoreLord and BPscoreLord correspond to the GOscoreLord for molecular function and biological process, respectively.This corresponds to the original definition form Lord et al. . MFscoreMycobacteria to functions from mammals. The distributions for IO, HSS, LSS, and NSS where calculated using the funSim score. The MFscore and the BPscore were used to calculate the corresponding GO score distributions for the IO set. The comparison of yeast with human proteins was done with the funSim score. In this comparison, we used the 7356 yeast proteins and the 70 447 proteins from human from UniProt release 5.8. Almost 3 000 proteins could not be analyzed because there is no GO annotation available. Another 1 300 proteins have either no molecular function or no biological process assigned, giving an incomplete score. The data files for the comparison of biological processes from fungi and mammals , the comparison of molecular functions from Mycobacteria and mammals , and the funSim comparison of yeast with human ("sc_hs.txt") are available for download at .We compared the biological processes from fungi to processes from mammals and the comparison of molecular functions from ) was used to perform metric multidimensional scaling (MDS). All yeast proteins with molecular function annotation were compared mutually yielding a square symmetric similarity matrix. Since the MFscore is a similarity measure and no distance, the distance of two proteins was computed as dmf = 1 - MFscore. The same procedure was applied to the molecular function annotation of the Pfam families. A square symmetric dmf matrix was used as input for the cmdscale method in R to perform a metric MDS. The normalized stress (NS) was calculated asThe statistical software environment R  was calculated aswhere k being the number of dimensions. Densities have been estimated with a two-dimensional Gaussian kernel estimation by the kde2d function from the R software.with ) and Python 2.4.2  using a maximum linkage clustering algorithm. The distance matrix was the same as used for the MDS.The hierarchical clustering was done with Pycluster version 1.29 , BPscore (B), funSim score (C) for different sets of protein pairs using GO annotation with all evidence codes.Click here for file


A benchmarking of the most popular orthologous identification methods using functional genomics data identifies the two best methods. The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the definition of orthology is incorrectly interpreted as a prediction of proteins that are functionally equivalent across species, while in fact it only defines the existence of a common ancestor for a gene in different species. However, it has been demonstrated that orthologs often reveal significant functional similarity. Therefore, the quality of the orthology prediction is an important factor in the transfer of functional annotations (and other related information). To identify protein pairs with the highest possible functional similarity, it is important to qualify ortholog identification methods.To measure the similarity in function of proteins from different species we used functional genomics data, such as expression data and protein interaction data. We tested several of the most popular ortholog identification methods. In general, we observed a sensitivity/selectivity trade-off: the functional similarity scores per orthologous pair of sequences become higher when the number of proteins included in the ortholog groups decreases.By combining the sensitivity and the selectivity into an overall score, we show that the InParanoid program is the best ortholog identification method in terms of identifying functionally equivalent proteins. Orthology is one of the central concepts of comparative genome analysis, but is often misused as a description of functionally equivalent genes in different species. By definition, the term describes the evolutionary relationship between homologous genes whose independent evolution reflects a speciation event, whereas paralogy refers to genes that have diverged from a common ancestor through a gene duplication event . OrtholoSo far, the genomes from more than 200 organisms have been fully sequenced. Of particular interest for medical research are the full genome sequences of human and model organisms, such as fruit fly, worm, mouse, rat, and chicken. Genome sequencing projects on other model organisms, such as the chimpanzee , are alsHomo sapiens. The reasons for this difference in inclusiveness are twofold. Firstly, there are differences between the algorithms being employed, such as bidirectional best hits, the triangular best-bidirectional hits scheme of the COGs 6] and MCThe 'best bidirectional hit' (BBH) method is the most frequently applied method to determine orthologous pairs. It assumes that a cross-species protein pair in which each protein gives back the other protein as being the best hit in the whole other proteome is an orthologous pair. In this research, the best bidirectional hits were determined based on Z-values of the Protein World human-mouse and human-worm set, without a sequence similarity cutoff. In total, 12,817 human-mouse and 5,714 human-worm orthologous pairs were identified. Although the BBH method theoretically can give some many-to-many orthologs, it practically gives only one-to-one orthologous pairs.In the INP method , all posThe KOG database  is the eThe MCL algorithm  starts wWithin the Z1H method, all cross-species protein pairs that have a Z-score of 100 or higher are considered to be orthologs. The Z-value estimates the statistical significance of a Smith-Waterman dynamic alignment score (SW-score) through the use of a Monte-Carlo process . In thisThe PGT method uses the output generated by multiple alignments and subsequent tree calculation  to definBelow are a description and the workflow of the used benchmarks. The first two benchmarks measure 'direct conservation of functional parameters', that is, they examine only one protein in human and one protein in mouse/worm. The last three methods compare the relationship between two proteins in human with the relationship of their two orthologs in mouse/worm .The results of the group orthology methods were analyzed in two ways: we determined the average score for all pairwise orthology relationships within an orthologous group; and we only considered the best scoring pair within an orthologous group. The latter option obviously leads to a much higher score for the many-to-many orthology relationships. However, by including only one pair of orthologous sequences per orthologous group, that high score is balanced by a reduction in the total number of orthologous relationships (one per orthologous group). Both the number of orthologous relationships and the quality of these relationships are taken into account in the final assessment of the ortholog identification algorithms.et al. The 'best hit' of the worm tissue samples for each human tissue sample was seen as corresponding tissue. These ten corresponding tissues were then used to calculate the Pearson correlation coefficients between the human and worm proteins, from which only the positive correlations were used. Proteome sizes were corrected for this by multiplying them by two, before calculating the average proteome size. For visualization reasons we displayed error bars of only one-eighth of the SD. Because of the differences between the human-mouse and human-worm expression data analyses, we emphasize that the two figures  database  was usedet al. [et al. [We defined a list of 'true ortholog pairs', for both human-mouse and human-worm, as a reference set. We chose the Hox cluster proteins and hemoglobins as a human-mouse reference set because of its well-studied evolution in vertebrates. We determined the homeobox orthologs using Figure et al. , resulti [et al. , resulti [et al. , which h [et al. , was anaFor each of these parts of our reference set and for each of the six ortholog identification methods, we determined how many of these orthologous pairs were covered, together with the number of false positives (pairs having only the human protein or the mouse/worm protein from a reference pair). Finally, to have a fair comparison between the several ortholog identification methods, we calculated the number of orthologous pairs divided by the average proteome size.The following additional data are available with the online version of this paper. Additional data file 1 contains all end data used to create the figures. Additional data file 2 contains all of the protein pairs that are considered to be 'true orthologs' within our ortholog reference set, consisting of several protein families. The first column contains the name of the protein family, the second the human gene names and the third the mouse/worm gene names. The fourth column contains the corresponding human 'Protein World' entries, whereas the fifth column contains the mouse/worm entries. The last columns contain the orthologous protein pairs.All end data used to create the figures.Click here for fileAll of the protein pairs that are considered to be 'true orthologs' within our ortholog reference set, consisting of several protein families. The first column contains the name of the protein family, the second the human gene names and the third the mouse/worm gene names. The fourth column contains the corresponding human 'Protein World' entries, whereas the fifth column contains the mouse/worm entries. The last columns contain the orthologous protein pairs.Click here for file


Since proteins perform their functions by interacting with one another and with other biomolecules, reconstructing a map of the protein-protein interactions of a cell, experimentally or computationally, is an important first step toward understanding cellular function and machinery of a proteome. Solely derived from the Gene Ontology (GO), we have defined an effective method of reconstructing a yeast protein interaction network by measuring relative specificity similarity (RSS) between two GO terms.Saccharomyces protein-protein interaction database called SPIDer. It houses a gold standard positive dataset (GSP) with high confidence level that covered 79.2% of the high-quality interaction dataset. Our predicted protein-protein interaction network reconstructed from the GSPs consists of 92 257 interactions among 3600 proteins, and forms 23 connected components. It also provides general links to connect predicted protein-protein interactions with three other databases, DIP, BIND and MIPS. An Internet-based interface provides users with fast and convenient access to protein-protein interactions based on various search features . In addition, the RSS value of two GO terms in the same ontology, and the inter-member interactions in a list of proteins of interest or in a protein complex could be retrieved. Furthermore, the database presents a user-friendly graphical interface which is created dynamically for visualizing an interaction sub-network. The database is accessible at .Based on the RSS method, here, we introduce a predicted SPIDer is a public database server for protein-protein interactions based on the yeast genome. It provides a variety of search options and graphical visualization of an interaction network. In particular, it will be very useful for the study of inter-member interactions among a list of proteins, especially the protein complex. In addition, based on the predicted interaction dataset, researchers could analyze the whole interaction network and associate the network topology with gene/protein properties based on a global or local topology view. Detailed reviews of these methods can be found elsewhere [Protein-protein interactions play a key role in many biological processes and therefore, are increasingly forming the focus of studies in understanding protein function and cellular behaviour ,2. Genomlsewhere ,16. Currlsewhere ,18 and hlsewhere .Originating from the Gene Ontology (GO), which organizes biological information for molecular function (MF), biological process (BP) and cellular component (CC) for different model organisms , we haveSaccharomyces Protein-protein Interaction Database (SPIDer). This database contains the predicted yeast protein-protein interaction dataset, which is based on a new release of GO and the annotations derived from SGD [Using our computational method, we have now developed a web-accessible database, from SGD . MoreoveWe used the MySQL database system and Perl scripting language to develop and implement our database. The graphical view of network was designed using the Graphviz tool . Our serSPIDer houses the data for yeast protein-protein interactions (GSP dataset) solely derived from the GO and yeast annotations by measuring RSS between two GO terms at the genome level  Searching by protein information: given an SGD  identifi(ii) Searching by GO term information: by entering a GO term identifier or a keyword of the term description, users can retrieve a list of proteins annotated on the term(s).(iii) Retrieving the RSS value of two GO terms in the same ontology by entering their identifiers. This option allows users to access the maximum RSS value computed between the two GO terms.(iv) Browsing inter-member interactions by entering a MIPS complex identifier or a list of proteins: we provide users with a search facility to extract the inter-member interactions of a list of proteins of interest. It allows inputting a MIPS complex identifier, and also a list of gene names, ORF names or SGD identifiers either by simply pasting them into the text box, or by uploading a plain file where proteins are separated by a space or a newline. For example, in order to identify the topology of SCF-CDC4 complex , the identifier is entered. Figure (v) Searching by sequence similarity: this search feature facilitates users to retrieve protein sequences matching a query sequence or its fragment. The results are returned as a list of proteins ordered by the BLAST e-value.The yeast protein-protein interaction dataset will continue to be updated using the new release of GO and yeast annotations in SGD. Furthermore, in the future, we intend that our database will provide more detailed analysis results of the whole interaction network, which are useful in analyzing and understanding yeast proteome. We also expect to reconstruct the maps of interactions from other completely sequenced genomes with high-quality GO-based annotations for functional genomic research.SPIDer is an open and public database server for protein-protein interactions which are solely derived from the Gene Ontology (GO), based on the yeast genome. It provides users with convenient access to protein interactions based on various search features. In particular, it allows users to analyze the potential inter-member interactions among a list of proteins of interest, which is especially useful for the analysis of protein complexes. Furthermore, it presents a graphical interface for visualizing an interaction sub-network, facilitating users to associate the network topology with gene/protein properties based on a global or local topology view.The database is freely accessed on the internet . The comSaccharomyces Genome DatabaseSGD: DIP: Database of Interacting ProteinsBIND: Biomolecular Interaction Network DatabaseMIPS: Munich Information Center for Protein SequencesXMW was responsible for the development and construction of the database and drafted the manuscript, while LZ designed and implemented the web interface. JG provided essential suggestions for designing the database and helped in drafting the manuscript. KL designed and coordinated the work. CF, HJZ, DD, ZBL and DYZ made their great efforts in acquiring and analyzing data, and in testing the database and the web interface. All authors read and approved the final manuscript.


Bioinformatics can be defined as the application of computational tools to manage and analyze biological data, typically at the molecular level. The field of Computational Biology aims at the mathematical or computational modeling of biological processes, such as protein folding or protein-protein interaction. Many books that include the title bioinformatics or computational biology (BCB) actually present a mixture of the two fields. It is sometimes not possible to separate the two as for a single problem such as protein folding both bioinformatics techniques such as similarity search of existing protein databases as well as computational biology is used in the form of mathematically modeling the protein structure via thermodynamic models that are optimized via a computational technique.Books in BCB can be categorized broadly into application tools books, which describe existing applications  to solve a biological problem . There aThe book reviewed here is different in the sense that it focuses on a single domain, namely protein bioinformatics. Ten problems in this domain are identified and discussed in 10 Chapters. The book starts with an introduction to proteins and their structure which is helpful for the uninitiated.Protein Sequence Alignment and provides a biological motivation based on the evolution of proteins. The standard algorithms for global (Needleman Wunsch) and local alignment (Smith Waterman) are presented. Chapter 2 discusses Feature Prediction of proteins including features for secondary structure, active sites, binding ligands or sites being recognized by enzymes. In this context hidden markov models, neural networks and support vector machines are discussed. The important principles of specificity and sensitivity are also introduced. In Chapter 3 the challenges of Protein Function prediction are discussed including the problem of absence of standards in new protein naming and protein function annotation. Another point of caution raised is the issue of error propagation in biological networks due to transferring functional annotations by similarity. Protein Structure Prediction is discussed in Chapter 4, which covers energy based modeling of protein structures and the limitations of such approaches, mainly based on the imperfections due to the classical approximation of quantum-mechanical interactions between protein atoms. In addition, a number of computational approaches to search the protein conformational space are discussed. Chapter 5 reviews Membrane Proteins and is more biological and less technically oriented than previous chapters. The topic of Chapter 6 is Functional Site Identification and the detection of functional sites in proteins and structural superposition are presented. Chapter 7 discusses Protein-Protein Interaction and explores sequence-based, structure based and experimental methods for detecting protein-protein interactions. The scoring of docking solutions and the CAPRI experiment are discussed as well. A related problem is presented in Chapter 8, which focuses on Protein-Small Molecule Interaction. A number of methods are presented and the implications to the virtual screening of drug compounds for the drug discovery process are discussed. Chapter 9 elaborates on Protein Design to synthesize proteins that do not necessarily exist in nature. This is also known as the inverse protein folding problem. Intuitive design, lattice models and automatic models are touched upon. The book concludes with a discussion of Protein Engineering in Chapter 10, which deals with engineering novel properties into naturally occurring proteins.Chapter 1 discusses the basic problem of Each chapter offers an introduction to the problem and discusses a number of solutions or solution attempts. It is nice that for proposed solutions either the biological rationale is given, or it is explained how the model's deviation from the biological reality affects the proposed solution. A summary of the reliability of the present methods, suggestions for promising avenues and a list of suggested readings conclude each chapter. The author needs to be commended for fairly discussing a great variety of computational approaches including dynamic programming, hidden markov models, monte carlo methods, simulated annealing, genetic algorithms, neural networks, and heuristic based methods including "intuitive design" as well as for discussing their benefits and drawbacks on selected problems. One of the clear strengths of the book is that it cautions us to accurately test the reliability of methods, assess the appropriateness of approximations and to be aware of the limitations of data produced in wet-lab experiments.This book is geared towards graduate students or bioinformatics researchers at an intermediate level. It is useful to people from either a life science background or a computational background and a bit of exposure to both areas is helpful. The book is comprehensive and succinct; people on busy schedules can read it in a short period of time. Of course, due to being succinct, it does not provide all the technical details of all discussed computational methods, but I do not see this as a drawback, because it does a great job at drawing interconnections between the related problems and related approaches. One reservation to be noted is that not all illustrations are of the same high quality. Particularly the high quality illustrations from the color insert are reproduced in grayscale where they are discussed in the text and this renders many of them hard to interpret. I believe that it would have been better to simply refer the reader to the color insert. Another issue is that the number of human genes is given as 50,000 (p. 53) and this is dated as more recent estimates are closer to below 30,000 . Neverth


Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available.Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure..NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at  Such crystal packing contacts are non-specific and have no biological function associated [Protein-protein interactions play important roles in many biological processes. Structural models of the complexes resulting from these interactions are necessary to understand those processes at the molecular level. Among the different techniques which can be employed to determine the structures of protein complexes, X-ray crystallography is still the most popular . Howeversociated . The detsociated -9.in vivo, whereas protomers of non-obligate complexes may dissociate from each other and stay as stable and functional units. Similarly, protein complexes have been divided as permanent or transient according to their lifetime.In addition, there are diverse types of biological interactions . Protome2 cutoff for interface size between biological interactions and non-biological interactions is used by PQS. Ponstingl and coworkers reported an optimal cutoff of 856 Å2 for differentiating homodimers and monomers [et al. introduced statistical learning methods to predict protein quaternary structures based on protein sequence information [A number of studies have examined properties of protein-protein interfaces in order to discriminate biologically relevant interactions and non-biological interactions resulting from crystal packing contacts. It has been shown that biological interactions tend to have larger interface size than non-biological interactions -6,11. PQmonomers ,6. Aminomonomers ,9,13,14.monomers . Zhang eormation .et al. identified residues involved in protein interaction sites based on hydrophobicity [et al. performed a statistical analysis of the interface properties for obligate and non-obligate interactions [Similar properties have been employed for identifying protein-protein interaction sites. Jones and Thornton analyzed six physicochemical interface properties and used them for predicting interaction sites ,16. Gallhobicity . Zhou anhobicity . Also, rhobicity -22. The hobicity . Differehobicity ,25. Somehobicity . This wohobicity . Recentlractions . They reractions . In obliractions . They acIn this paper, first we investigate six interface properties for a set of non-redundant protein-protein interactions. These properties are interface area, ratio of interface area to protein surface area, amino acid composition of the interface, correlation between amino acid compositions of interface and protein surface, interface shape complementarity, and conservation of the interface. Then we trained an SVM classifier with these interface properties to differentiate not only biological interaction from crystal packing contacts, but also obligate interactions from non-obligate interactions. We constructed a two-stage SVM to handle the three-class classification problem. Our SVM classifier achieved an accuracy of 91.8% using leave-one-out cross-validation on the non-redundant dataset containing 243 interactions.We compiled a non-redundant data set with three types of protein-protein interactions from several sources. Here, every interaction involves two protomers, which refer to the two polypeptide chains in the protein complex. There may be more than two protomers per complex, resulting in several interactions. When considering a protein-protein interaction, only the two protomers involved are relevant.Obligate interactions were taken from a previously compiled set . Non-oblA set of crystal packing contacts was compiled from the PDB in two steps. First we collected a non-redundant set of biological dimers from the PDB. We selected all dimeric complexes as defined in the PDB file sections REMARK 300 and REMARK 350. A similar procedure as described above was used to eliminate the redundancy in the set. The dimers were grouped according to the pair of SCOP families to which they belong. For each group the complexes with AEROSPACI scores below 0.5 were removed. The biological units for the remaining dimers were confirmed by manually inspecting the relevant literature. Then, for each group the dimer with the highest AEROSPACI score was selected. In total we collected 120 dimers. Second, for the selected 120 dimers we rebuilt unit cells and chose the largest non-biological interface in each unit cell for our final set of crystal packing contacts. We obtained 120 crystal packing contacts with this procedure, but for only 106 of them we could obtain conservation scores. In total, we gathered 243 protein-protein interactions of which 75 are obligate interactions, 62 are non-obligate interactions and 106 are crystal packing contacts. We will refer to this final dataset as BNCP-CS. The PDB ids are listed in Table 2 upon the formation of the complex [In order to characterize the different types of protein-protein interactions, we analyzed the following six interface properties: interface area, ratio of interface area to protein surface area, amino acid composition of the interface, correlation between amino acid compositions of interface and protein surface, gap volume index, and conservation score of the interface. A residue is defined as being part of the interface if its solvent accessible surface area (SASA) decreases by > 1 Å complex . A prote complex , with a Interface area is defined as one half of the total decrease of SASA (ΔSASA) of the two protomers upon the formation of the interaction:a and b are two protomers in the complex ab; SASAa, SASAb and SASAab are the SASA values for a, b, and ab, respectively. The native complex may contain additional protomers, but they are not considered.where Biological interactions that involve a small protomer cannot have large interface areas. This applies to some enzyme-inhibitor complexes, for instance. Therefore, we defined a new feature, in which the interface area is normalized by the SASA of the smaller protomer in the complex:a and SASAb are the SASA values for protomers a and b, respectively.where SASAvn) is defined as the frequency of each type of the 20 standard amino acids in the protein-protein interface. By weighting each residue with its ΔSASA, the area-based amino acid composition va is computed:We calculated both number-based and area-based amino acid composition . The numr) is the type of the amino acid of residue r.where type of the two-stage classification strategy, crystal packing contacts were separated from biological interactions. Then putative biological interactions were passed to the second stage (SVM2), where obligate and non-obligate complexes were distinguished , they were set to 0.004 and 128 for the SVM in the first stage, and 0.00085 and 512 for the SVM in the second stage.The R package e1071 ,43 inter [libsvm  was usedWe obtained posterior probabilities for our classification with the same R package. It fits a logistic distribution to the pairwise classification decision values using a maximum likelihood algorithm . With th2, 1170.7 Å2, and 435.9 Å2, respectively. The distribution of obligate IAs has the largest variance among the three sets. When using a cutoff of 650 Å2, approximately 7% of all instances are misclassified in a binary classification discriminating between biological interactions and crystal packing contacts. The three types of interactions exhibit considerable differences regarding this property.The histogram of IAs for the three types of interactions in the BNCP-CS dataset is shown in Figure The distribution of IARs for the BNCP-CS dataset is shown in Figure ν distances and correlation coefficients  also show different distributions in the obligate and crystal packing interfaces. Aromatic residues (FWY) tend to be more abundant in biological interfaces. We observed that Cysteine occurs more often in the biological interfaces than in crystal packing contacts. These residues also indicate that non-obligate interfaces exhibit intermediate characteristics between obligate interactions and crystal packing contacts, in particular for the sets of hydrophobic and charged residues.The overall area-based amino acid composition of the interfaces for the three types of complexes in the BNCP-CS dataset is reported in Figure Correlation coefficients calculated using both number-based and area-based amino acid compositions are reported in Figure It is shown in Figure Figure 2 and 32.6 Å2 for obligate and non-obligate interactions, respectively, but for crystal packing contacts this value is only 18.6 Å2.In Figure Scatter plots comparing different interface properties are provided in the supplementary material , false negative (FN), false positive (FP) and true negative (TN) are defined in Table andWe investigated the best performances of the two-stage SVM in terms of cross-validation accuracy when using combinations of six individual features: IA, IAR, AACa, CORa, GVI, and CSa .We have applied our best performing SVM, which is the two-stage SVM trained using three features , to the dataset used by Bahadur et al. . This daWe can reasonably expect that in this dataset the subset of homodimers mostly includes obligate interactions. In addition, inspecting the descriptions of the 70 other protein-protein complexes in the PDB files, one can expect that this subset mostly contains non-obligate interactions. The second stage SVM predicts 84.4% of the homodimers to be obligate, and 78.6% of the remaining complexes to be non-obligate. Although these results do not represent an actual validation, they do agree with our expectations.2 to be a reasonable cutoff of interface area for the binary classification of biological and non-biological interactions. This threshold separates the BNCP-CS dataset with an accuracy of 93%. Biological interactions where small protomers are involved are better identified using the interface area ratio property in addition.In this paper we analyzed five interface properties for three types of protein-protein interactions. Interface area remains one of the most important features for distinguishing biological interactions from crystal packing contacts. The area of a crystal packing interface is typically smaller than that of a biological interface Figure  DifferenThe 20 amino acids display variable preference for protein-protein interaction in terms of the number of residues taking part in the interaction and the ΔSASA involved in the total interface area. Obligate and non-obligate interactions show noticeable differences regarding the features based on amino acid composition.Residues involved in biological interactions were shown to be more strongly conserved than residues involved in crystal packing contacts Figure . With thet al. published the results of a statistical analysis of the interface properties for obligate and non-obligate interactions [As demonstrated in the section on the analysis of the interface properties, the non-obligate interactions in our datasets exhibit intermediate values for all interface properties except the interface area ratio. These results agree with the expected different stability of these types of interactions . Recentlractions . Our conThe first stage of the two-stage SVM classifier distinguishes crystal packing contacts from biological interactions with an accuracy of 97.9% (see the Two-stage SVM section). Valdar and Thornton obtained an accuracy of 98.3% on a similar problem . NeverthThe nested cross-validation results indicate that there is no considerable difference between the performances of the multi-class and two-stage SVMs. The small variances of these results along with the minor difference between the performances of the SVM implementations indicate that the approach is quite robust.The method based on atomic contact vectors described by Mintseris and Weng results in considerable accuracy (91%) in the classification of obligate and non-obligate interactions . We inteThis study is also related to the work of Bradford and Westhead, investigating different interaction types . Howeveri.e. obligate interactions, non-obligate interactions, and crystal packing contacts. These three types of interactions exhibit distinct interface properties.In this work we have analyzed several interface properties for three types of protein-protein interactions, To classify the three types of interactions, we have combined the properties using a support vector machine algorithm and implemented it as NOXclass. NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypothesis regarding the nature of protein-protein interactions, when experimental results are not available. We can expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists.A web server based on the method and the datasets used in this study are available at . Source NOXclass requires LINUX or UNIX operation system, as well as a Python interpreter.The NOXclass program uses NACCESS  to calcuIn addition, the NOXclass program uses SURFNET  to compuThe source code of the NOXclass program is distributed under the terms of GNU LGPL.A list of abbreviations used in this paper has been given in table HZ developed the method under the supervision of FD, IS and TL. HZ, FD and IS evaluated and interpreted the results. Every author contributed to the final version of the paper.supplementaryClick here for file


Saccharomyces cerevisiae, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe), contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from  How can we tackle the complexity of a living cell? It is commonly said that living organisms are complex and display “emergent” properties. Emergence is perceived in this context as behaviors that appear at the system level but are not observable at the level of the system's components. In the cell this would be equivalent to saying that the cellular complexity could be explained if we could understand the interplay between the cellular components: that is, not just describe the “parts” that make up a cell but understand how they interact with each other to perform the necessary tasks.A big step on the road to understanding cellular complexity will be a complete list of all relevant interactions between the cellular components. Although a lot of progress as been made in this direction, we are often dependent on experimental methods that are costly and time consuming. It's a big challenge for computational biology to process the current available knowledge and to propose new ways of predicting the interactions between cellular components.Here the researchers studied protein interactions that are mediated by small linear peptide motifs,specifically interactions between a protein's SH3 domain and its targets, usually small peptide stretches containing a PXXP motif (where P is proline and X is any amino acid). The results showed that the putative target motifs that are conserved in ortholog proteins and are within regions that do not have a defined secondary structure are more likely to be relevant binding sites. Besides proposing a way to combine secondary structure information with comparative genomics to predict protein–protein interactions, the researchers highlight a possible role of intrinsically disordered proteins in SH3 protein interactions. The results also show that when looking for conservation of these motifs, it is important to carefully select the species used in the study: comparisons between species that have diverged to a certain extent—not too little and not too much—are the most informative. Important advances have been made in using computational methods to mine the ever-growing quantity of experimental results in order to derive predictions of protein–protein interactions. For such interactions there are methods that explore sequence and structure analysis, like gene fusion ,2, gene The work described here deals with the prediction of protein interactions mediated by recognition modules that target small linear motifs ,16 and mIn this work we set out to obtain a high-confidence, biologically relevant protein interaction network, starting from the consensus information and using computational methods. The study showed that it is possible to greatly increase the accuracy of consensus-based predictions of protein–linear sequence interactions by taking into consideration the fact that biologically relevant target ligands of SH3 domains are more likely to be within disordered regions and conserved in orthologs. The method's performance was improved by selection of species within an optimal divergence time from the species of interest.Saccharomyces cerevisiae and provide information about the binding sites within the target proteins.It has been proposed that intrinsic disorder may play a role in protein interactions –26, and S. cerevisiae and in a set of thirteen yeast species: Candida glabrata, Debaryomyces hansenii, Kluyveromyces lactis, Yarrowia lipolytica [C. albicans [S. paradoxus, S. bayanus, S. mikatae [S. castellii, S. kudriavzevii,S. kluyveri [Neurospora crassa [Schizosaccharomyces pombe [Using profile hidden Markov models see , all putpolytica , C. albialbicans , S. para mikatae , S. castkluyveri , Neurospa crassa , and Sches pombe .S. castellii,S. kluyveri, and S. kudriavzevii, no orthologs for the majority of the S. cerevisiae SH3 domains could be identified (results not shown). However, these genomes had only been sequenced with a 2- to 3-fold coverage [In coverage , which mS. cerevisiae.The ortholog SH3 domains were split into three groups: conserved domain, possibly divergent  were excluded as these species would have lower statistical power. Therefore, the first group analyzed consisted of five yeast genomes that broadly covered the hemiascomycete phylum, containing the four recently reported genomes of C. glabrata, D. hansenii, K. lactis, and Y. lipolytica that we grouped with the C. albicans genome.To test the improvement of consensus-based predictions with a comparative genomics approach, an initial set of genomes was chosen based on the conservation analysis of the SH3 domains across the different yeast species . N. crasS. cerevisiae included in the two sets of positive standards (15 SH3 domains in the gold set and ten in the platinum set) were taken from the data published by Tong et al. [S. cerevisiae proteome. From this a measure of accuracy and coverage , we felt that the first approach was more stringent.Using T-Coffee , an aligS. cerevisiae sequence, and we called this the probing region. In In the orthologs, the search was restricted to a window surrounding the putative target ligand in the S. cerevisiae proteins. Using these two patterns, a loss in accuracy and coverage was observed (an average of 1.4 times less accuracy and 1.2 times less coverage for the PXXXPXXXP motif and an average of 3.4 times less accuracy and 2.5 times less coverage for the EXXPXXP motif). Thus, we can rule out the possibility that the results were generated by chance and can confirm that the observed phenomenon was the conservation of specific SH3 binding motifs and not of proline-rich tracks.Since most known target proteins in the SH3 interaction network are proline-rich and a large probing window was used, it is possible that the hits found in orthologs were due to chance and lacked biological meaning. To eliminate this possibility two “decoy” proline-rich patterns were analyzed: PXXXPXXXP and EXXPXXP (where X is any amino acid), different from the consensus sequences. Both patterns were found with high frequency (>400 hits) on p < 0.05) and not due to random sampling.However, the accuracy obtained with conservation alone was still poor . A hypergeometric test allowed us to say that that the improvement in both positive sets and for all conservation scores was significant  increase in the accuracy of protein target prediction (there was a 2-fold average increase in both sets) (Combining disorder prediction with comparative genomics resulted in a significant (th sets) . The decSince the platinum positive set was independent see , the valUsing the methods described in this work, we show proof of concept on how to integrate secondary structure prediction with comparative genomics to increase the accuracy of consensus-based prediction of peptide recognition modules. However, the method employed involves a clear trade-off between accuracy and coverage.p = 0.32 with a t-test), average proline content of protein targets (p = 0.12 with a t-test), usage of Class II motif (p = 0.21 with a hypergeometric distribution test), or conservation of SH3 domain (p = 0.82 with a t-test). There was a statistically significant difference in the average conservation of the target proteins (p = 0.03 with a t-test). The protein targets the method was able to predict were on average conserved in 8.7 of the ten species, while the targets not recovered were conserved in 7.6 species. This small but significant difference highlights the bias this method has for conserved interactions. A higher level of confidence can be placed in any putative target motif found conserved in most yeast species analyzed, but this level of conservation will only happen for essential interactions. It is important to note that for this reason this method will always miss species-specific protein interactions. However, adding more genomes of species within an appropriate divergence time should alleviate this problem, a concept discussed in more detail below. Another possible cause of loss in coverage could be interactions that are mediated by currently uncharacterized motifs or through noncanonical SH3 binding .Of the 59 interactions in the final high-confidence interaction presented by Tong et al. , the metAs shown by other authors (reviewed in ), it shoS. cerevisiae   .t-test determination was carried out to see whether the average frequencies within the highest and lowest combinations were significantly different from the frequency in all possible combinations. From the analysis of the results the more informative genomes are C. albicans, D. hansenii, C. glabrata,K. lactis, and Y. lypolytica. We can also see that N. crassa and Sch. pombe are not over-represented in the highest scoring groups, suggesting that they have less informative genomes. More importantly, it is clear that including the genomes of S. bayanus,S. mikatae, or S. paradoxus leads to a decrease in the accuracy of predictions. These observations correlate well with the degree of divergence observed for the SH3 domains . ains see  and withIn a recent report Eddy  used a tSch. pombe and N. crassa is small compared to species closely related to S. cerevisiae, we calculated the accuracy and coverage after addition of one or two of these species, to the five species selected previously, for different conservation scores. In general, an increase in coverage with little or no decrease in accuracy was observed . We believe that the improvement gained by adding species within the optimal divergence time would be better than that observed with N. crassa and Sch. pombe. The result generated with the latter two species suggests only that a sufficient number of genomes was not reached, since addition of more genomes still improved our scores. However, at present there are not enough genomes available to empirically tackle this question of a sufficient number of genomes for SH3 target prediction.It would be also interesting to study how many genomes would suffice to accurately predict an SH3 target interaction. Since the decrease in statistical power for rved see . AdditioWe believe the main factor determining the optimum divergence time is the conservation level of the biological feature. A biological feature that has higher conservation will require genomes of more divergent species to be accurately identified. Interaction types that are equally conserved should be accurately predicted with genomes of species at the same divergence times. This might mean that the same genomes could be used to predict interactions for other protein domains that bind small linear peptides . Other interaction types that are mediated by larger interaction surfaces are probably more conserved and therefore might require genomes from more divergent species.Although some results ,35 have In this paper we show that for the study of SH3 protein interactions the genomes with more relevant information are from species that diverged around 400–950My ago from the species of interest. As was suggested by Eddy , this opC. glabrata, K. lactis, C. albicans, D. hansenii, Y. lipolytica, N. crassa, and Sch. pombe to predict a set of 69 interactions regarding consensus sequence conserved in four of the seven genomes used (see We used the method described above and the genomes of S. cerevisiae. GO::TermFinder [p = 3.67 × 10−15), morphogenesis (p = 7.62 × 10−12), establishment of cell polarity (p = 1.19 × 10−11), actin cortical patch assembly (p = 5.09 × 10−9), and bud site selection (p = 1.28 × 10−8).As expected we obtained a highly interconnected network with a very significant over-representation of proteins participating in processes typically associated with SH3 domains in rmFinder  was usedS. cerevisiae biological processes these proteins were involved in. An interesting example is the proposed interaction between Abp1p with the P-type ATPases Dnf1p and Dnf2p. These proteins are required for phospholipid translocation and they mainly localize to the plasma membrane and intercellular compartments. The regulation of the lipid bilayer arrangement by Dnf1p and Dnf2p was demonstrated to be critical for budding endocytic vesicles [Some of the proposed interactions were further explored taking into account which vesicles . It is avesicles . FollowiS. cerevisiae include cell cycle, pheromone response, and filamentous growth). This reasoning actually leads us to think that the localization data on proteins are underevaluated and, if anything, will result in an underestimation of our accuracy scores.In order to calculate accuracy and coverage scores, we initially considered as “negative” interactions between proteins that did not share the same cellular compartment. After having obtained our list of predicted interactions, we decided to investigate them without disregarding these “negative” interactions. This decision was made because the negative set is based in part on high-throughput measurements that do not take into account the dynamics of cellular localization. Two proteins might not share a compartment in a given cellular condition, but this might change in different cellular states  by the overlap of the two.We considered a positive set of 59 interactions (containing 15 different SH3 domains from 15 different proteins) defined by Tong et al. ; this weWe considered a second positive standard, which we called the platinum set, of higher confidence, with 19 interactions (containing ten different SH3 domains from ten different proteins) derived from the overlap of the two-hybrid assays, obtained from Tong et al. , with thThe two positive datasets overlap only partially .S. cerevisiae proteins pairs that do not share at least one subcellular compartment.To build our negative dataset we assumed that two proteins that do not share the same subcellular compartment according to MIPS localization data  cannot iSince we also used the phage display data from Tong et al.  to derivWe did not merge the two positive datasets, thus keeping the platinum one as a truly independent positive dataset. We decided to also use the gold set because although it is not appropriate to use the absolute performance value calculated with this set to compare our method with others, it still served as a check for the relative performance of different filters of our method.The ratio between true positives (TP) and the sum of true positives plus false positives (FP) was used as a measure of accuracy. True positives were the number of predicted interactions within a positive set. False positives were the number of predicted interactions found within the negative set. To measure the coverage of the methods, we tracked the ratio TP/P, where P is the total number of positives in the positive set.S. cerevisiae were as follows: C. glabrata, 300 My; D. hansenii, 800 My; K. lactis, 400 My; Y. lipolytica, 900 My; C. albicans, 800 My; S. paradoxus, 50 My; S. bayanus, 50 My; S. mikatae, 50 My; N. crassa, 1,000 My; and Sch. pombe, 1,100 My. These values were based on phylogenetic studies found in the literature [The estimated divergence times of the other yeast species from terature ,42,43.Dataset S1The phylogenetic tree of all the SH3 domains of the yeast species in our study.(14 KB DND)Click here for additional data file.Table S1(248 KB PDF)Click here for additional data file.Table S2(248 KB PDF)Click here for additional data file.Table S3(17 KB PDF)Click here for additional data file.Table S4(52 KB PDF)Click here for additional data file.


Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods. Understanding biological processes requires knowing not only which proteins exist in a certain organism or cell type but also how these proteins interact with each other. However, determining the protein-protein interaction (PPI) networks is a daunting task and has been the subject of extensive research. Many computational and experimental techniques have been developed to observe or predict the PPI networks in biological systems -5. DespiTo overcome such problems, the use of various intersecting data types ,9,11-17 i in protein X and domain dj in protein Y is used to predict whether proteins X and Y interact. Corollary to this would be that for a given list of PPIs, can the possible interactions (and their strengths) between domain pairs be determined? This idea has been researched recently  where T was 9 in all cases except in initial studies testing the dependence on the scale. In each generation the population size is increased 10-fold by the use of recombination, mutation, and random-generation operations. A multi-point recombination function is used among randomly selected chromosomes to add 250 (5×) more chromosomes. Random mutations are carried out on the genes of the chromosome (parameters) to create 150 (3×) new chromosomes. Finally, 50 (1×) random chromosomes are created and added to the initial population. When combined, this set forms the population of a particular generation. In the later iteration stages of the GA run, the 50 seed chromosomes are selected by ranking the population according to the optimization fitness function and selecting the top 50 entries.Our GA runs used an optimization fitness function that describes how well the training PPI set is explained by the chromosome population. Each chromosome is an array of scores for the included domain-domain pairs, and this score set can be used to decide whether two proteins interact. Adapting the domain-domain interaction scores to predict PPIs requires the development of a criterion to decide which type of domain-domain score corresponds to a PPI. For this, we first form a list of all possible domain-domain interactions between two proteins; that is, all possible combinations between domain pairs. We then take either the largest (maximum score detection rule) or the sum  of the domain-domain interaction scores from this list to represent the strength of the interaction between the proteins. If the determined strength is larger than a pre-assigned cutoff (> 5 when T = 9), then that pair of proteins is predicted to interact. The pair is assumed to not interact if the score is below the cutoff (< 5 when T = 9), and an indecisive assignment is made if it is equal to the cutoff. These PPI predictions are then compared to the training data where correct prediction is granted + 1 point, and a penalty of -1 is applied for an incorrect identification. Indecisive assignments do not contribute to the optimization fitness score. As the number of positive and negative PPI entries in the training dataset can be vastly different, we normalize the contributions of the negative and positive PPIs to the overall fitness function according to the number of PPIs in each list such that both lists carry equal weight. The optimization fitness function is then maximized during the GA iterations, and when the score does not change over 15 successive iterations, the GA is terminated. At least 2000 GA runs starting from randomly selected populations were executed for each reported case. Results from the GA runs whose converged fitness function scores are low are considered unsuccessful, and those runs are discarded from the statistical analysis that determines the distribution and mean values of the optimized parameter values [e.g., Supplementary Table 2, in Additional file In the GA runs that use only the positive PPI list as the training data, we have also used a fitness function that minimizes the magnitude of the involved parameters. This fitness function has two terms. The first term represents the explanation ratio of the training dataset; it is exactly the same function that was discussed in the previous paragraph. The second term is the sum of the magnitude squares of the parameters; that is, the sum of squares of all domain-domain interaction scores. We multiply the second term with a weight factor α and then subtract it from the first term and use the resulting function as the fitness function in the GA runs. Results reported in Figure Success of a learning algorithm-based method depends on the quality of the available training data. For the PPI network construction studies, the training dataset ideally contains a list of truly positive interactions  as well as a list of non-interacting pairs of proteins . A well-performing interaction scoring scheme should have predictive power and be able to discriminate between the true and false observations. However, as briefly mentioned in the Background section, definite identification of true and false PPIs is problematic. Therefore, one has no choice but to create positive and negative PPI lists and assume that the list is correct.Fortunately, there are efforts devoted to construct PPI lists for yeast that are as reliable and correct as possible ,14,22. WCross-verification tests for DomainGA scores were performed using the Core and Full yeast PPI datasets from Uetz et al. . The CorThe datasets originally compiled by Rhodes et al.  were useclosed set is a subset of the PPIs such that included PPIs only contain the domain-domain pairs whose interaction scores are optimized in the runs. All the other PPIs are not selected. In contrast, the PPIs in which the involved protein pair has the potential to interact through one or more of the optimized domain-domain pairs are included in the inclusive set. In other words, PPIs in the inclusive set may interact through the domain-domain pair that is chosen as an optimization parameter, but these PPIs may have other domain-domain interaction pairs that are neglected in the optimizations. Details of the resulting set sizes are reported in Table These PPI datasets were further processed to obtain the relevant subsets Table  for use ij contain at least another domain pair whose interaction score is not optimized , then these PPIs will be excluded from the list defining the closed set. This would lead to the case that domain pair dij may not appear in any of the PPIs defining the closed dataset. When that happens, as there is no information that is relevant for its optimization, the value of this parameter will be set randomly during the optimization. This was observed in our simulations and, when T was 9, such parameters had average values in the [In forming the closed dataset, an additional problem arises which reduces the number of parameters that can be truly optimized with the DomainGA method. Say that a domain pair is chosen as a parameter to be optimized. If all the PPIs that include this is domain pair ds in the ,5 range We derive the domain information from the InterPro database ,53. Inte4 in InterPro classification, there are ~108 possible interacting domain-domain pairs. Reliable optimization of such large parameter sets requires PPI training data that are not currently available and possibly will not be available in the near future either. Therefore, inclusion of all possible domain-domain pairs in the optimization process is not realistic. To avoid parameter over-fitting, we have initially started with a very small parameter set and assumed that it is large enough to represent the important domain, and thus protein, interactions. To select the used domain-domain interaction parameter set, we have computed the histogram diagrams for the number of occurrences of the domain-domain pairs in the training PPI sets and sorted the pairs according to their occurrence counts to achieve reasonably large training and test datasets. This has allowed us to select sets with different number of domain-domain pairs /2 possible domain-domain pairs whose values need to be known. Noting that N is on the order of 10/2 possibOne very simple analysis method is to derive the domain interaction probabilities as the likelihoods that are computed using the relative occurrence statistics in the positive and negative PPI lists. Starting with the list of PPIs in the yeast MIPS dataset, we first obtain the domains of the proteins involved in the interactions and then create the unique list of all possible domains and domain-domain pairs amongst the protein-protein interactions. This gives the occurrence counts of each of the domains and the domain-pairs. Once the occurrence statistics is collected, the likelihood for each domain-pair in the set X (= negative or positive) was calculated by the following rule:2 = 0.713. Note that the calculated likelihoods can be improved using an iterative maximization approach such as the maximum likelihood estimation method that is discussed in the Results C section.The overall likelihood was represented as the log of the positive likelihood minus the log of the negative likelihood. The scores obtained with DomainGA and this simple likelihood analysis have a correlation of rHR conceived the idea, both authors designed the study and participated in its coordination, and MS performed the analysis. The authors wrote the paper together and approved the final manuscript.Table 1: This sheet lists the results for the MLE prediction  for various scenarios. Table 2a: This sheet lists the DomainGA parameter scores and their distributions. Table 2b: This sheet lists the names of the domains used in the DomainGA calculations.Click here for file


Non-invasive fluorophore-based protein interaction assays like fluorescence resonance energy transfer (FRET) and bimolecular fluorescence complementation  have been proven invaluable tools to study protein-protein interactions in living cells. Both methods are now frequently used in the plant sciences and are likely to develop into standard techniques for the identification, verification and in-depth analysis of polypeptide interactions. In this review, we address the individual strengths and weaknesses of both approaches and provide an outlook about new directions and possible future developments for both techniques. Oryza sativa) and thale cress  in hand [in planta, approaches is generally desired. Co-immunoprecipiatation , in vitn blots" , surfacen blots" ), blue nn blots" , biolumin blots"  and fluon blots" -12 are noperties , FRET anin planta protein-protein interaction assays, one should take some general considerations into account. First, it should be noted that all fluorophore-based methods require tagged variants of the POIs, modifications that may alter their physiological parameters. Thus, wherever possible, fluorophore-tagged POIs should be tested for bona fide subcellular localization, stability, and biological activity. The latter can for example be achieved by complementation of mutant phenotypes or, alternatively, by determining protein activities in in vitro assays. Since, conventionally, POIs can be tagged either N- or C-terminally, and since the site of tagging may determine experimental success in an empirical manner, all possible pair wise combinations should be tested when performing FRET or BiFC assays. Unfavorable circumstances, e.g. terminal targeting signals or transmembrane domains, may however preclude some of these theoretically possible combinations. It should be mentioned that, in principle, some POIs might also be tagged internally [Before starting any fluorophore-based ternally .A second aspect that needs to be considered is the expression level of the tagged POIs. Frequently, expression is driven by strong constitutive promoters  that may result in ectopic expression and/or overexpression. This might subsequently result in artifacts that may possibly either promote or inhibit particular protein-protein interactions. Thus, wherever possible, the native gene promoters should be used for driving the expression of fluorophore-tagged POIs. It should be stressed, however, that due to the method of gene transfer  even constructs with own promoters can result in overexpression when multiple gene copies are transferred into single target cells.in planta interaction assays. Since it is usually difficult to meet all the criteria mentioned above, one should at least attempt to fulfill as many as possible. In the ideal scenario, however, transgenic lines expressing both fluorophore-tagged POIs under control of their own promoters in a respective double mutant genetic background would be used.The target species and tissue for transgene expression should also be carefully selected. Ideally, the fluorophore-tagged POIs should be expressed in the homologous plant species and in a tissue type that is of biological relevance for the POIs and/or the anticipated protein-protein interaction. Wherever possible, expression should take place in respective (double) null mutants, since endogenous, untagged copies of the POIs may interfere with the protein-protein interaction assay, e.g. by out competing interaction partners. Lines homozygous for T-DNA insertions in the genes encoding both interaction partners represent thus suitable genetic backgrounds for in vivo. It might be debatable whether such a tight contact is the final proof of a true protein-protein interaction or, alternatively, represents merely an indicator of close vicinity, as for example, the co-localization of two polypeptides in a small plasma membrane microdomain  or co-pn blots"  or surfan blots" . In our Förster (or Fluorescence) Resonance Energy Transfer (FRET) is a biophysical phenomenon that was originally discovered more than half a century ago . Its occThe efficiency of energy transfer (E) is inversely proportional to the sixth power of the distance between the donor and the acceptor ,16:o)6}E = 1/{1 +  and thus in the range of conventional protein dimensions. The exact value of R0 is a function of the spectral overlap between donor emission and acceptor excitation spectra . A decrConventionally, FRET was determined by comparing the donor intensity of the donor-acceptor sample to that of the donor only sample, while concurrently comparing the acceptor intensity of the donor-acceptor sample to that of the acceptor only sample  represents a different procedure to document FRET. The method uses a spectroscopic rather than an image-based approach to quantify changes in the acceptor intensity at the donor excitation wavelength. This is achieved by recording emission spectra of the acceptor molecule in the absence or presence of the donor. In comparison to channel FRET, this approach is less sensitive to background noise since spectral rather than intensity information is used as readout. However, as in the case of sensitized emission (see above), a prerequisite is the expression of equal (equimolar) concentrations of the fluorophores, a condition that might be difficult to obtain. Despite this obstacle, the FSPIM procedure has been previously used in various plant FRET studies ,26,28,30via an as yet unknown mechanism [A further improvement to measure FRET is DFRAP, donor fluorescence recovery after photobleaching . This prechanism  – an incFinally, the most sophisticated but also a technically demanding way of determining FRET is by measuring the lifetime (the average time that the fluorophore spends in the excited state) of the donor in the presence and absence of the acceptor. This procedure exploits the biophysical fact that FRET leads to a decrease in the donor life-time that can be determined using suitable equipment. Fluorescence life-time imaging microscopy (FLIM) allows the measurement of changes in fluorophore life-times down to pico-second levels ,54. FLIMSeveral recent studies have used the potential of FLIM to capture interactions between partner proteins playing different roles in regulation of transcription, development as well as disease signaling in plants ,30,32,35Pairs of fluorophores with overlapping emission and excitation spectra are a prerequisite for FRET Figure . Ideallyde novo fluorescence. Thus, by BiFC, the interaction status of two POIs can be easily monitored via fluorescence emission upon excitation with a suitable wavelength  assay is based on the observation that N- and C-terminal sub fragments of GFP  do not spontaneously reconstitute a functional fluorophore. However, if fused to interacting proteins, the two non-functional halves of the fluorophore are brought into tight contact, refold together and generate  Figures  and 6.in vitro and in E. coli to study protein-protein interaction. Subsequently, Hu and co-workers showed that a split fluorophore reporter can also be used in mammalian cells [in planta have been published [As many other interaction reporter systems, the BiFC assay is a fragment complementation assay (FCA). GFP and its variants are especially attractive reporters for FCA-based interaction studies, because i) no exogenous reagent needs to be added to detect the reassembled protein and ii) GFP and its derivatives are known to express, fold, mature and fluoresce in virtually every cell type and subcellular structure in which they have been tested. Mutational studies uncovered permissive sites within the GFP molecule that allow insertions without disturbing GFP fluorescence ,64 and tan cells . Finallyan cells ,69. Meanublished -80 and tWhen using BiFC for interaction studies one should be aware of the pitfalls and limitations of this technique. One major drawback as well as an advantage of the BiFC approach lies in the irreversibility of complex formation . This obin planta expression generally contain the strong constitutive CaMV 35S promoter [via random mutagenesis, may diminish NAI-associated difficulties in the future.Another important aspect is to what extent overexpression may produce artefactual results in BiFC assays. Transfection studies with COS cells revealed that high amounts of vector DNA, containing N- and C-terminal YFP sensor peptides respectively, provoked unspecific fluorescence emission . In contpromoter ,69. It hpromoter . HoweverAlthough NAIs pose a problem in interaction studies, they are quite useful to determine the subcellular localization of a protein or a protein domain. In NAI-based localization studies, one sensor peptide is fused to the POI while the complementary sensor peptide is fused to a targeting signal for a suspected subcellular destination . If the compartment-targeted sensor peptide produces fluorescence in combination with a protein that is fused to the complementary sensor peptide, one can deduce that the investigated protein localizes to the sensor peptide-targeted compartment. This assay has been recently used to determine the topology of an integral membrane protein . In the in vivo, it is possible that a third protein mediates the interaction. In this case the observed interaction would be indirect. It has been estimated that fluorescence complementation can occur when fragments are fused to positions that are separated by a distance of approximately 10 nm (100 Å), provided that there is enough flexibility to allow reconstitution of the split YFP fragments [Based on its designation one might expect that BiFC provides a direct measure for bimolecular interactions. However, as for all FCAs that are carried out ragments . Due to ragments , suggestin planta interactor screens and thus the replacement of the conventional yeast two-hybrid assay. The envisaged BiFC-based interactor screen of cDNA expression libraries has already been carried out in a suspension cell culture of mammalian COS cells [Agrobacterium-mediated transient transformation of plant tissues  represents such a method that is based on single molecule detection. This technique involves recognition of two fluorophore-tagged polypeptide species in a sub-femtoliter measurement volume. The two polypeptides are marked with distinct fluorescent labels that can be separately excited and detected. Coincidence of signal fluctuations of both fluorophores in the detection volume indicates co-migration and thus association of the two proteins at the single molecule level [in vivo to date [in planta protein-protein interaction assays.During the past few years, FRET and BiFC have been established as reliable techniques for the analysis of protein-protein interactions in living plant cells. However, one of the obvious disadvantages of both approaches is the fact that they often involve ectopic expression and/or overexpression of the respective fusion proteins  will evBlue Fluorescent ProteinBFP Bimolecular Fluorescence ComplementationBiFC Cauliflower Mosaic VirusCaMV Cyan Fluorescent ProteinCFP Donor Fluorescence Recovery after PhotobleachingDFRAP Fragment Complementation AssayFCA Fluorescence Cross Correlation SpectroscopyFCCS Fluorescence Lifetime ImagingFLIM Fluorescence (Förster) Resonance Energy TransferFRET Fluorescence Spectral Imaging MicroscopyFSPIM Green Fluorescent ProteinGFP Non-Assisted InteractionNAI Protein of InterestPOI Red Fluorescent ProteinRFP Yellow Fluorescent ProteinYFP The author(s) declare that they have no competing interests.RAB wrote the draft for the FRET section, TL was the main author for the BiFC part. RAB designed schemes depicted in Figures